Cost-aware admission control for streaming media server

ABSTRACT

Systems and methods for managing admission of requests to a streaming media server are provided. In certain embodiments, a method comprises receiving a new request for a streaming file to be served by a media server, and determining a cost to the media server for serving the requested streaming file, wherein the cost corresponds to the media server&#39;s resources to be consumed in serving the requested streaming file. The method further comprises determining, based at least in part on the cost, whether to admit the new request for service by the media server. In certain embodiments, a method comprises receiving, at a time T cur , a new request for a streaming file to be served by a media server, and creating a segment-based model of the media server&#39;s memory as of time T cur . And, based at least in part on the segment-based model of the media server&#39;s memory, determining whether to accept the received request for service by the media server.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending and commonly assigned U.S. patent application Ser. No. 10/306,279 filed Nov. 27, 2002 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER,” the disclosure of which is hereby incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates in general to admission control strategies and more specifically to systems and methods for managing the admission of requests for streaming media files to a streaming media server.

DESCRIPTION OF RELATED ART

Today, much information is stored as digital data that is available to processor-based devices via client-server networks. Client-server networks are delivering a large array of information (including content and services) such as news, entertainment, personal shopping, airline reservations, rental car reservations, hotel reservations, on-line auctions, on-line banking, stock market trading, as well as many other services and types of content. Such information providers (sometimes referred to as “content providers”) are making an increasing amount of information available to users via client-server networks.

An abundance of information is available on client-server networks, such as the Internet, Intranets, the World Wide Web (the “web”), other Wide and Local Area Networks (WANs and LANs), wireless networks, and combinations thereof, as examples, and the amount of information available on such client-server networks is continuously increasing. Further, users are increasingly gaining access to client-server networks, such as the web, and commonly look to such client-server networks (as opposed to or in addition to other sources of information) for desired information. For example, a relatively large segment of the human population has access to the Internet via personal computers (PCs), and Internet access is now possible with many mobile devices, such as personal digital assistants (PDAs), mobile telephones (e.g., cellular telephones), etc.

An increasingly popular type of technology for providing information to clients is known as “streaming media” Streaming media is a well-known technology in the computer arts. In general, streaming media presents data (e.g., typically audio and/or video) to a client in a streaming or continuous fashion. That is, with streaming media a client is not required to receive all of the information to be presented before the presentation begins. Rather, presentation of information in a streaming media file may begin before all of the file is received by the client, and as the received portion of the file is being presented, further portions of the file continue to be received by the client for later presentation. Thus, streaming media involves media (e.g., typically audio and/or video) that is transmitted from a server (a media server) to a client and begins playing on the client before fully downloaded.

Streaming media is a particularly popular technique for communicating audio and/or video files from a server to a client. Audio and video files tend to be quite large, even after being compressed. If streaming media is not used, an entire file is generally required to be downloaded to a client before the client can begin to play the file. Such a download may require an undesirably long delay before the client can begin playing the file. With streaming media (e.g., streaming audio or streaming video), a client is not required to wait until the entire file is downloaded to play it. Instead, the client can begin playing the file (e.g., presenting the video and/or audio to a user) while it downloads to the client.

Streaming media has quickly become the most popular form of multimedia content on the Internet. Video from news, sports, and entertainment sites are more popular than ever. Media servers are also being used for educational and training purposes by many universities. Further, use of media servers in the enterprise environment is also gaining momentum. Many radio broadcasts are available over the Internet, which make use of streaming audio to enable a much wider audience access to their broadcasts.

In view of the above, the area of multimedia services in a networked environment is a rapidly expanding field in today's technological world. The delivery of continuous media from a central server complex to a large number of (geographically distributed) clients is a challenging and resource intensive task. Media servers are commonly implemented for providing streaming media to clients. Various streaming media files may be provided concurrently by a media server to various different clients. That is, a plurality of clients may concurrently access streaming media files from the media server. Of course, limits exist as to how many concurrent streams a media server can support for a given client population. That is, limits exist as to the capacity of a media server for supporting a given “workload” (i.e., a number of concurrent client accesses of streaming media from the media server).

BRIEF SUMMARY OF THE INVENTION

In certain embodiments of the present invention, a method for managing admission of requests to a streaming media server is provided. The method comprises receiving a new request for a streaming media file to be served by a streaming media server, and performing a resource availability check for the streaming media server to determine whether the streaming media server has sufficient available resources to service the new request. The method further comprises performing quality of service guarantee check for the streaming media server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the streaming media server for any previously accepted requests.

In certain embodiments, a method for managing admission of requests to a media server comprises receiving a new request for a streaming file to be served by a media server, and determining a cost to the media server for serving the requested streaming file, wherein the cost corresponds to the media server's resources to be consumed in serving the requested streaming file. The method further comprises determining, based at least in part on the cost, whether to admit the new request for service by the media server.

In certain embodiments, a system is provided that comprises a server having a memory, wherein the server is operable to serve at least one streaming file to clients communicatively coupled thereto. The system further comprises an admission controller operable to receive a new request for a streaming file to be served by the server, determine a cost to the server for serving the requested streaming file, wherein the cost corresponds to the server's resources to be consumed in serving the requested streaming file, and determine, based at least in part on the cost, whether to admit the new request for service by the server.

In certain embodiments, a method is provided that comprises receiving, at a time T_(cur), a new request for a streaming file to be served by a media server, and creating a segment-based model of the media server's memory as of time T_(cur). And, based at least in part on the segment-based model of the media server's memory, determining whether to accept the received request for service by the media server.

In certain embodiments, computer-executable software stored to a computer-readable medium is provided. The computer-executable software comprises code for creating a segment-based model of a media server's memory, and code for determining whether to serve a requested streaming file from the media server based at least in part on the segment-based model of the media server's memory.

In certain embodiments, a cost-aware admission control system is provided that comprises a means for receiving, at a time T_(cur), a new request for a streaming file to be served by a media server. The cost-aware admission control system further comprises a means for creating a segment-based model of the media server's memory as of time T_(cur), and a means for determining, based at least in part on the segment-based model of the media server's memory, whether to accept the received request for service by the media server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example client-server system in which embodiments of the present invention may be implemented;

FIG. 2 shows an example of how file segments are stored to memory when concurrent (or overlapping) accesses are made to different media files, given the real-time nature of such media files;

FIG. 3 shows an example in which concurrent (or overlapping) accesses are made to the same file f;

FIG. 4 shows an example operational flow diagram for determining a data structure that represents the “unique” segments of a file that were accessed during a time interval of interest in accordance with an embodiment of the present invention;

FIG. 5 shows an example of requests for file accesses that are made to a media server during the interval of time t₁ through time T, wherein the interval from time T^(mem) through time T can be determined that comprises the segments of accessed files that are currently stored to the media server's memory, which has size Size^(mem);

FIG. 6 shows an example of determining a time interval that includes file accesses such that the sum of unique bytes accessed is equal to the size of the media server's memory;

FIG. 7 shows an example operational flow diagram for determining the memory state of a media server from which the type of access required for satisfying a received request can be determined in accordance with an embodiment of the present invention;

FIG. 8 shows an example flow diagram of determining a media server's memory state and using the memory state to determine a type of access of a newly received request in accordance one embodiment of the present invention;

FIG. 9 shows an example operational flow diagram of one embodiment of an admission controller;

FIGS. 10A-10B show an example operational flow diagram for Quality of Service (QoS) validation that is performed by an admission controller in accordance with one embodiment of the present invention;

FIG. 11 shows an example of safely replaceable bytes that may be determined for a media server's memory;

FIGS. 12A-12B show performance results for two workloads applied in a sensitivity study to an example media server having a cost-aware admission controller of an embodiment of the present invention;

FIG. 13 shows an example Utility Data Center arrangement in which resources may be dynamically allocated and in which certain embodiments of the present invention may be implemented; and

FIG. 14 shows an example computer system on which embodiments of the present invention may be implemented.

DETAILED DESCRIPTION

Embodiments of the present invention provide a system and method for managing admission of requests to a streaming media server. For instance, a cost-aware admission control system for a media server is provided, wherein a cost is determined for a received request and such cost is used to determine whether the media server can support such request. In accordance with an embodiment of the present invention, the current memory state of a media server at any given time may be determined through a memory state model. Such modeling of the media server's memory state provides a close approximation of the real system memory but reflects a higher-level memory abstraction.

As described further below, certain embodiments of the present invention enable the memory state of a media server to be efficiently determined, which enables such modeling of the memory state to be used, for example, in implementing an admission control system for managing the acceptance of requests to be serviced by the media server. In accordance with embodiments of the present invention, from the modeled memory state, a determination of the streaming files (or file segments) present in the media server's memory at any given time can be intelligently estimated. Accordingly, a determination of the media server's resources that will be consumed in servicing a newly received request may be computed. That is, the memory state-may be used to efficiently determine whether the memory resources or the disk resources of a media server will be consumed in servicing a newly received request for a particular streaming file. Thus, a “cost” of the media server's resources required for servicing the received request (e.g., whether memory resources or disk resources will be utilized) may be determined for the request, and such cost may be utilized for determining whether the request should be admitted for service by the media server or whether admitting such request would result in overloading the media server such that the quality of service provided by the media server degrades below a desired level. Thus, modeling of the media server's memory state may be used for implementing an admission control policy for determining whether the media server can service a newly received client request without undesirably degrading the quality of service provided by the media server.

Certain embodiments of the present invention utilize a segment-based access model for representing unique, most recently accessed segments of a file. Such a segment-based access model may be used for efficiently computing a media server's memory state, as described further below. That is, considering that a streaming file may be concurrently accessed (or have overlapping accesses thereto), various portions (or segments) of the streaming media file may have different time stamps at which they have been most recently accessed by a client. As used herein, a streaming file is intended to encompass any type of file now known or later discovered that is served in a streaming fashion. Examples of a streaming file include well-known streaming media files, such as streaming audio and/or streaming video files.

For example, a first client may request, at time t=0 seconds, access to a streaming media file f that is 600 seconds in duration, and a second client may request access to such streaming media file f 10 seconds later, at time t=10 seconds. Given the real-time nature of streaming media files, at time t=50 seconds, the first client will have accessed 50 seconds of file f (i.e., the segment from 0 seconds to 50 seconds of file f), and the second client will have accessed 40 seconds of file f (i.e., the segment from 0 seconds to 40 seconds of file f). However, the second client's access of the segment 0-40 seconds of filed is more recent than the first client's access of that segment. That is, the second client's access of the segment 0-40 seconds of file f started at time t=10 seconds, whereas the first client's access of that segment started at time t=0 seconds. But, the first client has most recently accessed the segment 40-50 seconds of file f (i.e., such access of segment 40-50 seconds of file f was started by the first client at time t=40 seconds), and the second client has not yet accessed this segment at all. A segment-based access model of an embodiment of the present invention is utilized for file f to represent that segment 0-40 seconds of file f was most recently accessed starting at time t=10 seconds and segment 40-50 seconds of file f was most recently accessed starting at time t=40 seconds. Again, such a segment-based access model for streaming media files is utilized in certain embodiments of the present invention to enable a memory state of a media server to be efficiently computed.

Further, by computing the memory state from the segment-based access model in accordance with certain embodiments, a segment-based model of the media server's memory results. That is, a segment-based model of the media server's memory may be determined, as opposed to the real organization (e.g., “page” organization) of the memory. Such a segment-based model of the memory enables efficient determination of the content of such memory over time (e.g., determination of the file segments that are evicted from memory in favor of inserting new file segments into memory, etc.).

Thus, as described further below, an embodiment of the present invention provides a cost-aware admission control strategy for a media server that efficiently utilizes the available media server resources (e.g., the media server's capacity) to support an optimum number of requests (or an optimum “workload”) while ensuring that desired quality of service (“QoS”) guarantees are satisfied. Various embodiments of the present invention are now described with reference to the above figures, wherein like reference numerals represent like parts throughout the several views. Turning first to FIG. 1, an example client-server system 100 is shown in which embodiments of the present invention may be implemented. Client-server system 100 comprises a plurality of clients 104, 105, and 106, and a media server (or complex of media servers) 101. As used herein, a media server (or “streaming media server”) is intended to encompass any processor-based device now known or later developed that is capable of serving one or more streaming files to clients thereof. Of course, such a media server (or “streaming media server”) may be further operable to also serve other types of files to its clients. Clients 104-106 are each capable of communicatively accessing media server(s) 101 via communication network 103. Communication network 103 is preferably a packet-switched network, and in various implementations may comprise, as examples, the Internet or other Wide Area Network (WAN), an Intranet, Local Area Network (LAN), wireless network, Public (or private) Switched Telephony Network (PSTN), a combination of the above, or any other communications network now known or later developed within the networking arts that permits two or more computing devices to communicate with each other.

Media server(s) 101 of the illustrated embodiment comprise data storage 102 for storing streaming media files, such as File A, File B, and File C. Data storage 102 may comprise internal or external disk drives, floppy disks, optical disks, Compact Discs (CDs), Digital Versatile Discs (DVD), memory, and/or other data storage devices now known or later developed for storing digital data. For instance, as described further below, data storage 102 typically comprises at least disk resources and memory resources for storing streaming media files (or segments thereof). In operation, a client may request a streaming media file available from media server 101, and such media server 101 may serve the streaming media file as a stream to the requesting client via communication network 103. That is, a client may request a particular content (e.g., audio, video, or other type of content) and media server 101 may serve a streaming media file as a stream to provide the requested content to the requesting client.

Media server 101 may comprise streaming media file(s) that is/are encoded for transmission at each of a plurality of different bit rates. For example, a streaming media file, File A, may comprise a particular content and it may be encoded for transmission at a plurality of different bit rates, such as 28 Kb/s, 56 Kb/s, and/or various other bit rates. Each resulting version of the file encoded for transmission at a given bit rate may be stored to data storage 102, e.g., File A₁ encoded for transmission at 28 Kb/s and File A₂ encoded for transmission at 56 Kb/s may each be stored to data storage 102 (note that files A₁ and A₂ comprise substantially the same content but are encoded for transmission at different bit rates, and thus the quality of each file may differ). As used herein, a file encoded for transmission at a particular bit rate may be referred to as a file encoded at the particular bit rate. In common phraseology in the art, a streaming media file is referred to as being “encoded at a particular bit rate”, which means the file is encoded for transmission from the server at the particular bit rate. Thus, as used herein, the phrase “encoded at a bit rate” when describing a streaming media file means the streaming media file is encoded for transmission at the bit rate, as is consistent with common phraseology in the streaming media art.

Media server 101 generally attempts to serve the most appropriate encoded file to a client based at least in part on the client's access speed to the client-server network 103. For example, suppose client 104 has a 28 Kb/s speed connection to communication network 103, client 105 has a 56 Kb/s speed connection to communication network 103, and media server 101 comprises File A₁ encoded at 28 Kb/s and File A₂ encoded at 56 Kb/s stored to data storage 102; when client 104 requests the content of File A, media server 101 typically attempts to serve File A₁ to client 104 (as File A₁ is the highest-quality encoded file supportable by client 104's connection speed), and when client 105 requests the content of File A, media server 101 typically attempts to serve File A₂ to client 105 (as File A₂ is the highest-quality encoded file supportable by client 105's connection speed). However, in the above example, suppose that media server 101 does not have File A encoded at 56 Kb/s but instead comprises File A₁ encoded at 28 Kb/s and File A₂ encoded at 112 Kb/s; then upon client 105 requesting the content of File A, media server 101 typically attempts to serve File A₁ to client 105, as File A₁ is the highest-quality encoded file providing the requested content that is encoded at a suitable bit rate that client 105's connection can support.

Typically, a streaming media player is executed by the requesting client for playing the received stream. Various types of streaming media technologies exist. Popular streaming media players include those provided by RealNetworks™ (see http://www.realnetworks.com), such as its RealPlayer™ and RealOnePlayer™ streaming media players, and that used by Microsoft's Windows® Media Player (see http://www.microsoft.com), as examples. For instance, clients 104, 105, and 106 comprise streaming media players 104A, 105A, and 106A, respectively, executing thereon for playing received streams in the example of FIG. 1. Typically, each streaming media player has a buffer associated therewith, such as buffers 104B, 105B, and 106B shown in the example of FIG. 1, for buffering received streamed data to improve the continuity of the playback of such streamed data by the streaming media player.

As an example of a typical scenario for a user accessing an audio stream via the web, the user will typically use a web browser, such as Netscape's Navigator™, Microsoft's Internet Explorer™, or other web browser now known or later developed, to request access to audio content (e.g., a RealPlayer sound clip) by, for example, clicking a link to such audio content provided on a web page being served by a web server. Assuming, for instance, that the requested audio content is included in a RealPlayer sound file, the web server sends back to the browser a file called a RealPlayer metafile, which is a small text file that has the true location (e.g., the Universal Resource Locator (URL)) of the requested RealPlayer sound file and also has instructions instructing the web browser to launch the RealPlayer sound player. For instance, client 104 may submit request 107 for desired streaming audio content by, for example, clicking on a hyperlink to such streaming audio content. If a suitable streaming media player 104A (e.g., a RealPlayer media player in the above example) is not already executing on client 104, media server 101 may return instructions launching such streaming media player 104A.

The streaming media player 104A contacts the URL contained in the received metafile. It should be recognized that often the URL contacted for accessing the desired audio stream is not on the web server from which the audio file was requested. Rather, the audio stream is often on a different media server (e.g., a RealPlayer server in the above example) designed to deliver streaming media files. For instance, in FIG. 1, client 104 may access a web server (not shown) and a user may click on a hyperlink provided on a web page being served by such web server to request desired audio content. The web server may return a metafile to the client indicating the URL of the corresponding sound file and launching streaming media player 104A if it is not already executing. The URL of the sound file may identify media server 101, which is designed for delivering streaming media files, such as those stored to data storage 102, and streaming media player 104A may therefore contact media server 101 (via request 107 in this example).

Media server 101 (e.g., a RealPlayer server in this example) and streaming media player 104A (e.g., a RealPlayer media player in this example) may interact with each other so that the server knows at what speed client 104 is connected to the Internet. If the connection is a low-speed connection, a streaming audio file encoded at a lower bit rate is typically sent. This will generally be a file of lesser quality than a file encoded at a higher bit rate and destined for a high-speed connection. If a high-speed connection is used, a larger, higher-quality sound file encoded at a higher bit rate is typically sent, which will generally provide for better sound quality. The requested audio file is then sent in Internet Protocol (IP) packets, typically using either the User Datagram Protocol (UDP) or the Internet's normal Transmission Control Protocol (TCP). UDP does not keep re-sending packets if they are misplaced or other problems occur, as does TCP, which may be preferable for certain streaming media technologies.

Thus, media server 101 serves the requested audio content to client 104 as stream 108. The packets are sent to buffer 104B on client 104, and once the buffer is filled, the packets begin being sent from the buffer to streaming media player 104A and it begins playing the audio file. As the file is being played remaining portions of the file are still transmitted from media server 101 to client 104 and are buffered in buffer 104B. If packets are not replenished to buffer 104B fast enough, then interruptions in the playback by the streaming media player 104A may occur, thus degrading the quality of the audio stream perceived by the user.

Steaming video may be communicated from media server 101 to a client in a similar manner as that described above for streaming audio. For instance, client 105 may submit request 109 for desired streaming video content. The requested video file is then sent in IP packets, typically using UDP. Thus, media server 101 serves the requested video file to client 105 as stream 110. The packets are sent to buffer 105B, and once the buffer fills up, the video begins being played by streaming media player 105A. As the file is being played, remaining video packets of the file are still transmitted from media server 101 to client 105 and are buffered in buffer 105B. If packets are not replenished to buffer 105B fast enough, then interruptions in the playback by streaming media player 105A may occur, thus degrading the quality of the video stream perceived by the user. Once all of the video data has been received and played by streaming media player 105A, the video stops. The video file generally does not remain on the client's system, but rather each section of the file is typically discarded after it is played.

As further shown in FIG. 1, client 106 may request streaming media content (e.g., a streaming audio and/or video file) from server 101 via request 111, and media server 101 may provide the requested content as stream 112 to client 106. While three clients are shown in this example, it should be recognized that many more clients may, in certain implementations, be concurrently accessing one or more streaming media files from media server 101. As described above, limits exist as to how many concurrent streams media server 101 can support for a given client population. Thus, it may become desirable to measure the capacity of a media server for supporting workloads applied thereto.

In order to measure the capacity of a media server for supporting a received client request, for example, it may be desirable to determine the impact that servicing such received client request will have on the media server's resources. That is, it may be desirable to determine whether servicing the received client request will impact the media server's memory resources or disk resources. As described further below, embodiments of the present invention provide a cost-aware admission control system for managing the admission of requests to be serviced by a media server. Certain embodiments utilize a memory model for efficiently modeling the media server's memory, wherein such memory model may be used to determine whether a requested file can be served from the media server's memory or whether such requested file is not present in memory and must be served from the media server's disk. From such determination of whether the requested file can be served from the media server's memory, a resource “cost” may be determined for the request, and such cost may be used in an admission controller to determine whether the request should be accepted for service by the media server.

Further, while a client requests streaming media content in each of the above examples, it should be recognized that in some implementations a streaming media file may be “pushed” from media server 101 to a client without the client specifically requesting such file. For instance, upon a client visiting a particular web page, the web page may cause a streaming media file (e.g., an advertisement) to be sent to the client. It should be understood that embodiments of the present invention are applicable to any such utilization of streaming media.

While examples of streaming media technologies, such as typical techniques for accessing RealPlayer streaming media files, are described above, it should be recognized that the present invention is not limited to a specific type of streaming media technology. Further, while examples of streaming audio and streaming video files are described above, the present invention is not limited solely to such types of streaming media, but rather any type of streaming file is intended to be within the scope of the present invention.

There are different types of media servers available in the art. Examples of known media servers include RealNetwork™ Server 8, Windows™ Media Server, QuickTime™ Streaming Server available from Apple Computer, Inc. In servicing client requests, some media servers, such as early versions of the Windows™ Media Server, completely bypass memory. However, many media servers, such as RealNetwork™ Server 8 use the system's memory in servicing client requests. For instance, many media servers use the native operating system file buffer cache support for buffering recently accessed portions of files being served to clients. Files available in memory can generally be accessed more efficiently than those stored to disk. If the file is not available in memory, then the media server accesses its disk to retrieve the file. As the file (or currently needed portion thereof) is retrieved from disk, it is stored to memory and served to the requesting client. In this manner, many media servers use the system's memory (e.g., native operating system file buffer cache support) for buffering recently accessed portions of files being served to clients to enable later accesses to those portions of files that are stored to memory to be served more efficiently by the media server. Embodiments of the present invention are useful for modeling the memory of such media servers that make use of memory for buffering recently accessed portions of files being served to clients, wherein such modeling may be used for implementing an admission control system for managing the admission of newly received requests for the media server.

Co-pending U.S. patent application Ser. No. 10/306,279 filed Nov. 27, 2002 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER,” the disclosure of which is hereby incorporated herein by reference, discloses a set of benchmarks for measuring the basic capacities of streaming media systems. The benchmarks allow one to derive the scaling rules of server capacity for delivering media files which are: i) encoded at different bit rates, and ii) streamed from memory versus disk. As U.S. patent application Ser. No. 10/306,279 titled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER” further describes, a “cost” function can be derived from the set of basic benchmark measurements. This cost function may provide a single value to reflect the combined resource requirement such as CPU, bandwidth, and memory necessary to support a particular media stream depending on the stream bit rate and type of access (e.g., memory file access or disk file access).

Traditional media server solutions do not have a built-in admission control policy (for controlling the admission of new client requests to be serviced by the media server) that can prevent server overload and guarantee a desired quality of service. Instead, traditional media servers continue to admit new client requests upon their receipt by the media server and provide degraded quality of service by sharing the available server resources among the admitted streams (even when the media server's resources are overloaded and unable to support the admitted streams).

Certain admission control strategies have been proposed in the technical literature that propose using the maximum bandwidth a server is capable of delivering as an indicator for admission control. However, as described in U.S. patent application Ser. No. 10/306,279 titled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER”, the amount of bandwidth a server is capable of delivering is variable and depends on the encoding bit rates of current streams in progress. Another possible approach for admission control strategy is based on a pessimistic model, where a new client request is admitted only if a server has enough resources to deliver this request from the disk. However, media server performance may be 3-5 times higher (depending on disk and file system) when media streams are delivered from memory versus from disk. Thus, while the pessimistic admission control strategy provides hard guarantees against server overload, it may lead to significant over-provisioning and inefficient resource usage. Thus, this strategy may indeed be too “pessimistic” because it has been observed that typical media workload has a high degree of reference locality (i.e., a high percent of requests often are accessing a small subset of media files) and exhibit a strong sharing pattern (i.e., accesses to the same file come in “bursts”). Intuitively, in such a workload, most of the accesses to popular files may be served from memory, rather than from disk.

Therefore, in order to provide an efficient admission control mechanism for a streaming media server, it is desirable to be capable of estimating the cost of a new request acceptance, and hence to be able to determine which segments of the requested file are stored in memory. More specifically, by determining which segments of a requested file are stored in memory, an estimate of consumption of memory versus disk resources of the media server in serving the requested file can be estimated. Accordingly, certain embodiments of the present invention enable a media server's current memory state to be modeled to enable, for example, a determination of whether a newly received request will initially be serviced by memory. That is, whether the received request will have an immediate impact on memory resources or disk resources of the media server can be efficiently determined from the modeled memory state. More particularly, the modeled memory state may identify the segments of files stored to the media server's memory (i.e., the content of the memory), and thus from such modeled memory state a determination can be made whether a prefix of a requested file is available in memory. If the prefix of a requested file is in memory then such request, if admitted for service by the media server, will have an immediate impact on the memory resources, whereas if the prefix of the requested file is not in memory then such request, if admitted for service by the media server, will have an immediate impact on disk resources. Thus, as described further below, a memory state model may be used to implement a cost-aware admission control strategy for managing admission of requests to a streaming media server.

A. Segment-Based Access Model

To efficiently determine a memory state of a media server, certain embodiments of the present invention utilize a segment-based model (or data structure) of streaming media files, as described further below. That is, an embodiment of the present invention that uses a segment-based model representing streaming media file accesses is described further below, wherein for each client request r the following notations are used:

-   -   file(r)—the media file requested by r;     -   duration(r)—the duration of file (r) in seconds;     -   bitRate (r)—the encoding bit rate of the media file requested by         r (in describing this embodiment, it is assumed that files are         encoded at constant bit rates);     -   t^(start)(r)—the time when a stream corresponding to request r         starts (once r is accepted by the media server);     -   t^(end)(r)—the time when a stream initiated by request r is         terminated (in describing this embodiment, non-interactive         client sessions are assumed, i.e.,         duration(r)≦t^(end)(r)−t^(start)(r)).

As an example of the real-time nature of streaming media, let request r be a sequential access to streaming media file f from the beginning of the file. For simplicity, let it be a disk access. Then, after 10 seconds of access r, the content, transferred by a server, corresponds to the initial 10 seconds of the file f. The duration of a transferred file prefix defines the number of bytes transferred from disk to memory and further to the client: in this example, it is assumed to be 10 seconds×bitRate(r). Moreover, the real-time nature of file access in streaming media defines the relative time ordering of streamed file segments in memory. This means that the time elapsed from the beginning of the file (e.g., 0 seconds is used here to denote the file beginning) can be used to describe both: 1) the streamed file segment and 2) the relative timestamps of this file segment in memory.

To clarify this point further, FIG. 2 shows an example of how file segments are stored to memory when concurrent (or overlapping) accesses are made to different media files, given the above-described real-time nature of such media files. For the example of FIG. 2, consider a media server that has a 100 MB memory, and the media server has media files f₁, f₂, and f₃ stored thereto, wherein each of such media files f₁, f₂, and f₃ are 600 seconds (10 minutes) long and encoded at 100 KB/s. Let us consider the following sequence of request arrivals as shown in FIG. 2:

-   -   request r₁ for file f₁ arrives at time t₁=0 seconds (sec);     -   request r₂ for file f₂ arrives at time t₂=100 sec; and     -   request r₃ for file f₃ arrives at time t₃=200 sec.

It should be recognized that the time reference shown in FIG. 2 is a “global” time reference across all of the received requests. For instance, request r₁ for file f₁ arrives at time t₁=0 sec, and request r₂ for file f₂ arrives 100 seconds later at time t₂=100 sec As described further below, the file segments may be designated by their “local” time references. For instance, at time t=150 sec file f₁, which began being accessed at time t₁=0 sec, is 150 seconds into its stream (which is 600 seconds in duration), and file f₂, which began being accessed at time t₂=100 sec, is 50 seconds into its stream (which is also 600 seconds in duration).

Let us evaluate the media server's memory state at time point T_(cur)=500 sec, shown in FIG. 2. While the overall number of bytes transferred by the three requests is 120 MB, the memory of this example media server can hold only 100 MB of latest (most recent) portions of transferred files which are represented by the following file segments:

-   -   a) a segment of file f₁ between 150 seconds and 500 seconds of         its duration. The denotation <150,500>(150) is used herein to         describe this segment, where numbers in “< >” describe the         beginning and the end of a file segment, and a number in “( )”         defines a relative timestamp (or “global timestamp”) in memory         corresponding to the beginning of the file segment. That is, the         numbers in “< >” describe the “local” timestamp of a file         segment by identifying the beginning and the end times of the         file segment, and the number in “( )” defines a “global”         timestamp for a time reference that is consistent across all         requests;     -   b) a segment of the file f₂: <50,400>(150), i.e., the segment of         file f₂ from 50 seconds to 400 seconds of its duration with the         access of such segment starting at the global timestamp of 150         seconds; and     -   c) a segment of the file f₃: <0,300>(200), i.e., the segment of         file f₃ from 0 seconds to 300 seconds of its duration with the         access of such segment starting at the global timestamp of 200         seconds.

This new abstraction provides a close approximation of file segments stored in memory and their relative time ordering (timestamps) in memory. That is, for the example of FIG. 2, at time T_(cur)=500 sec, the following segments are stored to the media server's memory: filed f₁<150,500>(150), file f₂<50,400>(150), and file f₃<0,300>(200).

The above example of FIG. 2 provides an example of modeling a media server's memory for concurrent accesses (or “overlapping” accesses—i.e., one access beginning before another access ends) to different files f₁, f₂, and f₃. In many instances, the overlapping accesses may be for the same file f. If there are multiple concurrent (or overlapping) accesses to the same file f, then requests with later arrival time might find the corresponding file segments being already in memory. Thus, operations for computing the unique segments of file f with the most recent timestamps which correspond to a sequence of accesses to f may be utilized in an embodiment of the present invention, as described further below.

In accordance with an embodiment of the present invention, a file segment transferred by request r^(f) during time interval [T, T′] is defined as follows: segm(r ^(f) ,T,T′)=<x,y>({circumflex over (T)}) Where x=max{T,t ^(start)(r ^(f))}−t ^(start)(r ^(f)), y=min{t ^(end)(r ^(f)),T′}−t ^(start)(r ^(f)), and {circumflex over (T)}=max{T,t ^(start)(r ^(f))}.

In computation of a current memory state in accordance with an embodiment of the present invention, the “unique” file segments currently present in memory are computed. This means that in a case of multiple requests to the same file, the accesses and the corresponding file segments with the latest access time are identified in a manner that avoids repetitively counting of the same bytes accessed by different requests at different time points. Thus, file segments of a model (or data structure) are referred to herein as being “unique” because the corresponding portion of the file of each segment is included in only one of the segments of the model. That is, each segment of the access model represents a unique portion of a file, and as described further below, each segment has associated therewith a corresponding global timestamp that identifies the time at which such segment was last accessed.

As a simple example, suppose a first request r, is received at global time t=0 sec for a file f, which is 100 sec in length. Assuming no other requests are made in the interim, at global time 100 sec the entire file f has been served to the requesting client and saved to the media server's memory. Accordingly, a segment of the memory may be denoted as f <0,100>(0), which identifies that segment <0,100> of file f (the entire file f in this example) is stored to memory having a latest access time of global timestamp t=0 sec. Assume now that at global time t=150 sec a second request r₂ for file f is received by the media server. Because file f is available in memory, it can be served to the client from memory. However, the memory segment for file f should now be updated to reflect that it has been more recently accessed. Thus, the data structure modeling such segment may be updated to provide: f<0, 100>(150), which identifies that segment <0,100> of file f (the entire file f in this example) is stored to memory having a latest access time of global timestamp t=150 sec. By updating the global timestamp at which the file segment was most recently accessed, a proper determination may be made as to the file segments (or portions thereof) that may be evicted from the media server's memory in accordance with the memory management scheme implemented for the server, such as a Least Recently Used (LRU) scheme, as described further below.

To explain this situation in more detail, attention is now directed to FIG. 3, which graphically depicts an example in which concurrent accesses are made to the same file f. In the example of FIG. 3, let r₁ ^(f), r₂ ^(f), r₃ ^(f) be a sequence of requests accessing the same file f in the following arrival order: t^(start)(r₁ ^(f))=0 sec, t^(start)(r₂ ^(f))=10 sec, and t^(start)=(r₃ ^(f))=20 sec. That is, request r₁ ^(f) is received at global time T=0 sec, request r₂ ^(f) is received at global time T=10 sec, and request r₃ ^(f) is received at global time T=20 sec.

By the time T′=50 sec, the first request r₁ ^(f) has transferred segment <0,50>(0) of file f, and the initial part of this segment <0,40>(0) is again accessed and transferred at a later time by the second request r₂ ^(f). Thus, segment <40,50>(40) is the only “unique” segment of file f accessed by r₁ ^(f) most recently. That is, segment <40,50>(40) is the only segment of file f that has been accessed most recently by request r₁ ^(f) because other segments of file f accessed by request r₁ ^(f) have been more recently accessed by other requests, such as request r₂ ^(f).

Similarly, segment <30,40>(40) represents the only unique segment of file f that was accessed most recently by r₂ ^(f). More specifically, by the time T′=50 sec, request r₂ ^(f) has transferred segment <0,40>(10) of file f, and the initial part of this segment <0,30>(10) is again accessed and transferred at a later time by the third request r₃ ^(f). Thus, segment <30,40>(40) is the only unique segment of file f accessed by r₂ ^(f) most recently. That is, segment <30,40>(40) is the only segment of filed that has been accessed most recently by request r₂ ^(f) because other segments of file f accessed by request r₂ ^(f) have been more recently accessed by other requests, such as request r₃ ^(f).

Finally, the latest request r₃ ^(f) is accountable for the most recent access to the initial segment <0,30>(20) of file f. Thus overall, the unique segments of file f with the most recent timestamps in the global time reference [0, 50]sec interval are the following:

-   -   segm(f, 0, 50)={<0,30>(20), <30,40>(40), <40,50>(40)}.

In the above denotation, segm(f, 0, 50) identifies that the segment under evaluation is the segment for file f as accessed during the global time reference of 0 sec to 50 sec. In the example of FIG. 3, the resulting segments of file f for this evaluation are {<0,30>(20), <30,40>(40), <40,50>(40)}, wherein the segment <0,30>(20) corresponds to the portion of file f most recently accessed by request r₃ ^(f), the segment <30,40>(40) corresponds to portion of file f most recently accessed by request r₂ ^(f), and the segment <40,50>(40) corresponds to portion of file f most recently accessed by request r₁ ^(f).

To determine the unique, most recent segments of file f accessed by subsequent requests r_(i) ₁ ^(f) and r_(i) ₂ ^(f) in [T, T′] time interval, a new operation called “segments subtraction” and denoted as “\” is introduced herein. Let r_(i) ₁ ^(f) and T_(i) ₂ ^(f) be two subsequent requests accessing the same file f such that t^(start)(r_(i) ₁ ^(f))≦t^(start)(r_(i) ₂ ^(f)), i.e. r_(i) ₂ ^(f) is more recent access than r_(i) ₁ ^(f). Let segm_(i) ₁ =segm(r_(i) ₁ ^(f), T, T′)=<x_(i) ₁ , y_(i) ₁ >(T_(i) ₁ ) and segm_(i) ₂ =segm(r_(i) ₂ ^(f), T,T′)=<x_(i) ₂ , y_(i) ₂ >(T_(i) ₂ ). Then

$\begin{matrix} {{{segm}_{i_{1}}\backslash{segm}_{i_{2}}} = \left\{ {{\begin{matrix} {{< x_{i_{1}}},{y_{i_{1}} > \left( T_{i_{1}} \right)}} & {{{if}\mspace{14mu} y_{i_{2}}} \leq x_{i_{1}}} \\ {{< y_{i_{2}}},{y_{i_{1}} > \left( T_{i_{1}}^{\prime} \right)}} & {otherwise} \end{matrix}{where}\mspace{14mu} T_{i_{1}}^{\prime}} = {T_{i_{1}} + {\left( {y_{i_{2}} - x_{i_{2}}} \right).}}} \right.} & (1) \end{matrix}$

Intuitively, operation (segm_(i) ₁ \segm_(i) ₂ ) tries to define a part of older segment segm_(i) ₁ , which does not coincide with any part of more recent segment segm_(i) ₂ . Accordingly, this operation results in determination of “unique” file segments that have been accessed.

Now, let r₁ ^(f), r₂ ^(f), . . . , r_(n) ^(f) be a sequence of requests accessing the same file f during [T,T′] interval, where t^(start)(r₁ ^(f))≦t^(start)(r₂ ^(f))≦ . . . ≦t^(start)(r_(n) ^(f)), i.e. r₁ ^(f) is the oldest access and r_(n) ^(f) is the most recent access to file f in [T, T′] interval. It is desirable to compute the unique segments of file f with the most recent timestamps which correspond to requests r₁ ^(f), r₂ ^(f), . . . r_(n) ^(f) during time interval [T, T′]. The general formula to compute such file segments is defined in the following way:

$\begin{matrix} {{{segm}\left( {f,T,T^{\prime}} \right)} = {\overset{n}{\bigcup\limits_{i = 1}}\left( {{{segm}\left( {r_{i}^{f},T,T^{\prime}} \right)}\backslash{{segm}\left( {r_{i + 1}^{f},T,T^{\prime}} \right)}} \right)}} & (2) \end{matrix}$

-   -   where segm(r_(n+1) ^(f), T,T′)=<0,0>(0), i.e. a “zero” size         segment. If r^(f) is the only request accessing file f during         [T,T′] interval then segm(f,T,T′)=segm(r^(f),T,T′).

As a further illustrative example, let segm(f, T, T′)={<x₁,y₁>(T₁), . . . , <x_(n),y_(n)>(T_(n))}). Note that the set of segments segm (f,T,T′) can be ordered in two different ways: 1) according to file f's structure and/or 2) according to their timestamps, as described further hereafter. The first ordering technique is referred to as “file structure ordering of the segments,” in which the segments are ordered according to file f's structure. In an embodiment of the present invention, the segments are ordered according to file f's structure if for any two consecutive segments <x_(i),y_(i)>(T_(i)) and <x_(i+1),y_(i+1),>(T_(i+1)) the following condition holds: y_(i)≦x_(i+1), 1≦i≦n−1. This representation conveniently reflects which segments of file f were accessed during the time interval [T,T′]. To distinguish this file structure ordering, the denotation

segm_(order)^(file)(f, T, T^(′)) is used herein.

The second ordering technique is referred to as “timestamp ordering of segments” (or “memory ordering of segments”), in which the segments are ordered according to their global timestamps. In an embodiment of the present invention, the segments are ordered according to their global timestamps if for any two consecutive segments <x_(i),y_(i)>(T_(i)) and <x_(i+1),y_(i+1)>(T_(i+1)) the following condition holds: T_(i)≦T_(i+1), 1≦i≦n−1. This representation conveniently reflects the time ordering of accessed file segments during the time interval [T,T′]. To distinguish time ordering, the denotation

segm_(order)^(time)(f, T, T^(′)) is used herein.

The above-described computations may be used to create a segment-based access model for the streaming media files, which can be used to efficiently determine the file segments stored in memory (i.e., to compute a current memory state of the media server). More specifically, the above-described computations can be used to create data structures representing segments of a file accessed by one or more requests during a given time interval of interest, and such segment-based model of the file accesses can be used to efficiently determine the current memory state of a media server.

For instance, FIG. 4 shows an example operational flow diagram for determining a data structure that represents the “unique” segments of a file that were accessed during a time interval of interest in accordance with an embodiment of the present invention. Operation starts in operational block 401, and in operational block 402 a time interval of interest is determined. For instance, a user or an application, such as an admission control application, may specify a time interval of interest. In operational block 403, the requests accessing a file f during the time interval of interest are identified. That is, the requests for file f received by the media server during the time interval of interest are identified.

In block 404, it is determined whether at least one request was made for file f during the time interval of interest. If not, then it is determined that there were no requests made for file f during the time interval of interest in block 405 and operation ends at block 410.

If it is determined in block 404 that at least one request was made for file f during the time interval of interest, operation advances to block 406 whereat it is determined whether multiple requests were made for file f during the time interval of interest. If not, then the segment of file f accessed by the single request during the time interval of interest is determined in block 407—recall from above that if r^(f) is the only request accessing file f during [T, T′] interval then segm(f, T, T′)=segm(r^(f), T, T′), and operation advances to block 409, which is described further below.

If it is determined in block 406 that multiple requests accessed file f during the time interval of interest, operation advances to block 408 whereat the unique, most recent segments of file f accessed by the requests are determined. For instance, in the example of FIG. 3, a time interval from 0 seconds to 50 seconds is of interest (i.e., [0,50]), and requests r₁ ^(f), r₂ ^(f), r₃ ^(f) are identified as being made for file f during such time interval. As described above, the unique, most recent segments of file f accessed by requests r₁ ^(f), r₂ ^(f), r₃ ^(f) in the example of FIG. 3 are {<40,50>(40), <30,40>(40), <0,30>(20)}. Again, these segments are referred to herein as being “unique” because the corresponding portion of the file of each segment is included in only one of the segments of the access model (or data structure). That is, each segment represents a unique portion of file f, and as described above, each segment has associated therewith a corresponding global timestamp that identifies the time at which such segment was last accessed.

As shown in the example of block 408, in certain embodiments blocks 408A and 408B may be performed for determining the unique, most recently accessed segments of file f. In block 408A, the segment of file f accessed during the time interval of interest by each request is determined. For instance, in the example of FIG. 3, a segment <0,50>(0) is accessed by request r₁ ^(f), a segment <0,40>(10) is accessed by request r₂ ^(f), and segment <0,30>(20) is accessed by request r₃ ^(f) during the time interval [0,50].

In block 408B, segment subtraction is performed to determine the unique, most recently accessed segments of file f For instance, in the example of FIG. 3, segment subtraction is performed, and it is determined that the unique, most recent segments of file f accessed by requests r₁ ^(f), r₂ ^(f), r₃ ^(f) are {<40,50>(40), <30,40>(40), <0,30>(20)}. As described above, the unique segments of file f with the most recent timestamps corresponding to requests r₁ ^(f), r₂ ^(f), . . . r_(n) ^(f) during a time interval [T,T′] of interest may be computed using the formula:

$\begin{matrix} {{{segm}\left( {f,T,T^{\prime}} \right)} = {\overset{n}{\bigcup\limits_{i = 1}}\left( {{{segm}\left( {r_{i}^{f},T,T^{\prime}} \right)}\backslash{{segm}\left( {r_{i + 1}^{f},T,T^{\prime}} \right)}} \right)}} & (2) \end{matrix}$ where

segm(r_(n + 1)^(f), T, T^(′)) =  < 0, 0 > (0), i.e. a “zero” size segment.

In operational block 409, a data structure representing the segments of file f accessed during the time interval of interest (e.g., segm(f,T,T′)) is created. For instance, continuing with the example of FIG. 3, a data structure segm(f,0,50)={<40,50>(40), <30,40>(40), <0,30>(20)} is created. As described above, in certain embodiments, the segments of segm (f,T,T′) may be ordered in two different ways: 1) according to file f's structure and/or 2) according to the segments' respective timestamps. Operation may then end in block 410.

B. Computing Memory State for a Media Server

As described with FIG. 4 above, data structures may be created that represent the unique, most recently accessed segments of a file for accesses occurring during a time interval of interest. In accordance with an embodiment of the present invention, such data structures may be used to compute (or model) the current memory state of a media server. As an example, suppose a media server has files f₁, f₂, f₃, f₄, and f₅ stored thereto, which are each encoded at the same bit rate (e.g., 56 Kb/s). Further suppose that the files have the following durations: f₁=45 sec duration, f₂=200 sec duration, f₃=360 sec duration, f₄=240 sec duration, and f₅=100 sec duration. The above-described modeling technique may be performed to create a segment-based access model for each file. For instance, a segment-based access model may be created for each of files f₁-f₅ based on accesses to those files during the time interval [0,5000] (i.e., from global timestamp 0 sec through global timestamp 5000 sec). The resulting segment-based access models that may be created for each of the files in this example are as follows:

-   -   segm(f₁, 0, 5000)={<0,40>(4960), <40,45>(4995)};     -   segm(f₂, 0, 5000)={<0,200>(270)};     -   segm(f₃, 0, 5000)={<0,360>(4500)};     -   segm(f₄, 0, 5000)={<0,240>(3560)}; and     -   segm(f₅, 0, 5000)={<0,100>(1025)}

Thus, a segment-based access model (or data structure) is constructed for each of the files f₁-f₅ that identifies unique, most recently accessed segments of each file during the time interval [0,5000]. As can be seen in the above segment-based access model for file f₁, its segment <0,40> was last accessed starting at time 4960 sec and its segment <40,45> was last accessed starting at time 4995 sec. From the segment-based access model of file f₂, it can be seen that its entire file (i.e., segment <0,200>) was last accessed starting at time 270 sec. From the segment-based access model of file f₃, it can be seen that its entire file (i.e., segment <0,360> was last accessed starting at time 4500 sec. From the segment-based access model of file f₄, it can be seen that its entire file (i.e., segment <0,240>) was last accessed starting at time 3560 sec. Finally, from the segment-based memory model of file f₅, it can be seen that its entire file (i.e., segment <0,100>) was last accessed starting at time 1025 sec.

Suppose now that the media server's memory has a size that is capable of storing up to 400 seconds of the streaming media files (i.e., memory size=400 seconds×56 Kb/s=22400 Kb in this example), the above segment-based access models may be useful in determining the data stored to the media server's memory at time t=5000 sec. For simplicity, this example assumes that all of the files have the same encoding bit rate. Of course, in many streaming media applications the streaming media files may have different encoding bit rates, and thus the amount memory consumed by an access to any such file may be determined as a function of the duration of the file access and the file's encoding bit rate (e.g., memory consumed for an access=file access duration×file encoding bit rate). In this example, all of files f₁-f₅ cannot be stored in fall to the media server's memory because the sum of the bytes accessed for such files f₁-f₅ (i.e., 945 sec×56 Kb/s encoding rate=52920 Kb in this example) exceeds the total bytes capable of being stored to the media server's memory (i.e., 22400 Kb in this example). Thus, it may be desirable to know the content of the media server's memory at time t=5000 (i.e., it may be desirable to know the portions of files f₁-f₅ that are stored to the media server's memory at time t=5000).

With knowledge regarding the media server's memory management scheme, the above segment-based access model of files f₁-f₅ may be used to determine the state of the media server's memory at time t=5000. For instance, typically a media server's memory is managed in accordance with an LRU scheme, wherein the most recently accessed segments of files are stored to memory and the oldest (or least recently) accessed segments of files are evicted when needed to make room in memory for storing more recently accessed files. Assuming such an LRU scheme is followed for the above example, the state of the media server's memory at time t=5000 can be determined using the segment-based access models of files f₁-f₅. For instance, from the above segment-based access models of files f₁-f₅, it can be seen that portions <40,45> and <0,40> of file f₁ were most recently accessed, i.e., at times 4995 sec and 4960 sec respectively. Thus, file f₁ having a duration of 45 sec is included in the media server's memory at time 5000 sec in this example.

The next most recent file segment accessed in the above example was the entire file f₃ (i.e., segment <0,360>) which was accessed at time 4500 sec. File f₃ has a total duration of 360 sec, while the duration of file f₁ is 45 sec. Thus, the sum duration of files f₃ and f₁ is 405 sec. Because each of the files are assumed in this example to have an encoding bit rate of 56 Kb/s, the entire 405 sec duration of files f₁ and f₃ exceeds the media server's memory. Thus, the entire 300 sec duration of file segment <0,300> of file f₃ cannot be stored to the media server's memory, but rather only the most recent 295 sec of such segment is stored to the media server's memory. The remainder of such segment, as well as the least recently accessed segments of files f₂, f₄, and f₅, would have been evicted from memory in order to store the more recent accesses in accordance with the LRU management scheme. Thus, the portion of segment <0,300> of file f₃ that would remain in memory is <5,300>(4505)—it should be noted that the timestamp for the access of this segment is 4505 sec. Thus, the resulting contents of the media server's memory in the above example would be as follows:

-   -   memory state={f₁<40,45>(4995),f₁<0,40>(4960),f₃<5,30>(4505)}.

Computing the current memory state may be described as follows: let Size^(mem) be the size of a media server's memory in bytes and let r₁(t₁), r₂(t₂), . . . , r_(k)(t_(k)) be a recorded sequence of requests to the media server; given the current time T, some past time T^(mem) is computed such that the sum of the bytes stored in memory between T^(mem) and T is equal to Size^(mem). In this manner, the files' segments streamed by the media server between times T^(mem) and T will be in the media server's memory. By modeling the current state of the media server's memory, an intelligent determination of the server's resources that will be utilized to service a newly received client request can be made. That is, an intelligent determination can be made as to whether a newly received client request can be serviced from the media server's memory or whether the received client request will require access to the media server's disk. An example of computing a current memory state of a media server in accordance with an embodiment of the present invention is described further below in conjunction with FIGS. 5-8.

Turning to FIG. 5, an example of requests for file accesses that are made to a media server during the interval of time t₁ through time T is shown, wherein the interval from time T^(mem) through time T can be determined that comprises the segments of accessed files that are currently stored to the media server's memory, which has size Size^(mem). More specifically, accesses r₁, r₂, . . . , r_(k−1), r_(k) are made during the time interval from time t₁ through the current time T. As shown in the example of FIG. 5, the total size of the segments accessed is greater than the total size Size^(mem) of the media server's memory. Thus, depending on the type of memory management scheme implemented for the memory, some of the accessed segments are evicted from the memory. That is, not all of the accessed segments can be stored to memory because the segments' total size is greater than the size Size^(mem) of memory. Typically, a LRU scheme is implemented for a media server, wherein the most recently accessed segments are stored to memory and the oldest (or least recently accessed) segments are evicted when necessary to make room for more recently accessed segments to be stored in memory. To determine the current contents of memory, the time interval from time T^(mem) to the current time T in which unique file segments that have a size totaling size Size^(mem) is determined.

Because, as described above, the function segm(f, T^(mem), T) represents the unique segments of file f accessed in [T^(mem), T] interval, the total amount of unique bytes of file f accessed and stored in memory between [T^(mem), T] interval can be computed, and such total amount is denoted herein as UniqueBytes(f,T^(mem), T).

To determine a media server's memory state in an efficient way, an embodiment of the present invention utilizes an induction-based algorithm for computing the memory state at any given time. As an example, let T_(cur) be a current time corresponding to a new request

r_(new)^(f), arrival, wherein an admission controller may be implemented to decide whether to accept or reject request

r_(new)^(f) for processing. Further, let T_(prev) denote the time of the previous arrival event, and let

T_(prev)^(mem) be a previously computed time such that the sum of bytes stored in memory between

T_(prev)^(mem) and T_(prev) is equal to Size^(mem), as shown in FIG. 6. It should be understood that while the duration from

T_(prev)^(mem) to T_(prev) is shown in FIG. 6 as being the same as the duration from

T_(cur)^(mem) to T_(cur), this will not always be the case. Indeed, often the duration of interval

[T_(prev)^(mem), T_(prev)] will be different than the interval

[T_(cur)^(mem), T_(cur)]. For instance, if the files accessed during each time interval are encoded at different rates, the durations from

T_(prev)^(mem) to T_(prev) and from

T_(cur)^(mem) to T_(cur) in which the amount of unique bytes accessed in each time segment is equal to Size^(mem) will not be the same. It should be recalled that to determine the amount of memory consumed by requests (i.e., to identify whether the requests fill the Size^(mem)) each file request may be computed as a function of the file's encoding rate and duration of its access (e.g., encoding rate of file×duration of access of the file).

Upon receipt of

r_(new)^(f) at time point T_(cur), the following are computed:

-   -   a) an updated time

T_(cur)^(mem) such that the sum of bytes stored in memory between

T_(cur)^(mem) and T_(cur) is equal to Size^(mem); and

-   -   b) an updated information on file segments stored in memory in         order to determine the access type of new request

r_(new)^(f) (e.g., whether the requested file will begin being served from memory or from disk if request

r_(new)^(f) is admitted to the media server).

For illustration, let Files(T_(prev))={f_(i) _(l) , . . . , f_(i) _(k) } be a set of files accessed during

[T_(prev)^(mem), T_(prev)] interval, and let Reqs(T_(prev)) be a sequence of requests r₁, r₂, . . . , r_(n) accessing files Files (T_(prev)) during

[T_(prev)^(mem), T_(prev)] interval. To compute the current memory state in time T_(cur) in accordance with an embodiment of the present invention, the following operations are performed:

-   -   1) Using Formula (2) from above, for each file f ε         Files(T_(prev)), all of the corresponding requests         Regs^(f)(T_(prev))={r₁ ^(f), r₂ ^(f), . . . , r_(n) ^(f)|r₁         ^(f)ε Reqs(T_(prev))} are considered, and all of the unique         segments of f which were accessed by requests from         Regs^(f)(T_(prev)) in

[T_(prev)^(mem), T_(cur)] interval are determined. Using file structure ordering of segments, the information about the files and their unique segments are stored in a data structure called

FileTable(T_(prev)^(mem), T_(cur)):

$\begin{matrix} {f_{i_{1}}\text{:}\mspace{14mu}{{segm}_{order}^{file}\left( {f_{i_{1}},T_{prev}^{mem},T_{cur}} \right)}} \\ \ldots \\ {f_{i_{k}}\text{:}\mspace{14mu}{{{segm}_{order}^{file}\left( {f_{i_{k}},T_{prev}^{mem},T_{cur}} \right)}.}} \end{matrix}$

-   -   2) Using timestamp ordering of segments, the information about         the files and their unique segments are also stored in a data         structure called

TimeFileTable(T_(prev)^(mem), T_(cur)):

$\begin{matrix} {f_{i_{1}}\text{:}\mspace{14mu}{{segm}_{order}^{time}\left( {f_{i_{1}},T_{prev}^{mem},T_{cur}} \right)}} \\ \ldots \\ {f_{i_{k}}\text{:}\mspace{14mu}{{{segm}_{order}^{time}\left( {f_{i_{k}},T_{prev}^{mem},T_{cur}} \right)}.}} \end{matrix}$

-   -   3) At this point, all of the necessary information is available         to compute an updated time

T_(prev)^(mem) such that the sum of unique bytes transferred during the period

[T_(prev)^(mem), T_(cur)] is equal to Size^(mem).

Using

FileTable(T_(prev)^(mem), T_(cur)), the total amount of unique bytes accessed during this time interval is computed as:

${{UniqueBytes}\left( {T_{prev}^{mem},T_{cur}} \right)} = {\sum\limits_{f \in F_{prev}}{{{UniqueBytes}\left( {f,T_{prev}^{mem},T_{cur}} \right)}.}}$ The difference

UniqueBytes(T_(prev)^(mem), T_(cur)) − Size^(mem) defines by “how much”

T_(prev)^(mem) should be advanced to a new time point

T_(cur)^(mem). Using

TimeFileTable(T_(prev)^(mem), T_(cur)), which provides the information about file segments according to their time ordering,

T_(cur)^(mem) can be determined.

After that, the corresponding data structures TimeFileTable and FileTable are updated for time interval

[T_(cur)^(mem), T_(cur)] to contain only file segments starting at time

T_(cur)^(mem) and later. Data structures Files(T_(cur)) and Reqs(T_(cur)) are also updated as follows: a) Files(T_(cur)) has only files with segments accessed at time

T_(cur)^(mem) and later, and b) Reqs(T_(cur)) has only requests which contribute to a set of unique file segments in

[T_(cur)^(mem), T_(cur)] interval.

From the determined memory state, a determination can be made as to whether a file f (or its initial prefix) requested by

r_(new)^(f) is residing in memory or not, and thus whether the request

r_(new)^(f) will have a type of access to memory or disk correspondingly.

FIG. 7 shows an example operational flow diagram for determining the memory state of a media server from which the type of access required for servicing a received request can be determined in accordance with an embodiment of the present invention. In operational block 701, a time interval is determined that includes file accesses that include an amount of unique bytes equaling the size of memory. For instance, as described above with FIG. 6 a time interval

[T_(cur)^(mem), T_(cur)] is determined. In operational block 702, the unique segments of files accessed during the determined time interval

(e.g., during  [T_(cur)^(mem), T_(cur)]) are determined. Thus, once this determination is made in block 702, the file segments that are in memory as of the end of the determined time interval (e.g., as of T_(cur)) are known. That is, the memory state of the media server as of the end of the determined time interval is known. In operational block 703, the determined unique segments of block 702 may be used to determine a type of file access that will be initiated for a client request if such request is initiated at the end of the determined time interval (e.g., at T_(cur)). More specifically, the determined memory state (or unique file segments determined as residing in memory) may be evaluated to determine whether, if a requested file access is admitted to the media server, such requested file will result in the file beginning to be served from the media server's memory or whether it will result in an access of the media server's disk being initiated.

Turning now to FIG. 8, an example flow diagram of one embodiment of the present invention is shown in greater detail. As shown, at block 801, a new request

r_(new)^(f) requesting access to a streaming media file f is received by a media server (or admission controller) at time point T_(cur). In block 802, a time interval

[T_(cur)^(mem), T_(cur)] is determined that includes file accesses with an amount of unique bytes equaling the size Size^(mem) of the media server's memory.

As further shown in FIG. 8, the time interval

[T_(cur)^(mem), T_(cur)] may be determined in block 802 through operations 802A-802E. In block 802A, the unique segments and corresponding access timestamps for files accessed during

[T_(prev)^(mem), T_(cur)] interval are identified. For illustration, let Files(T_(prev))={f_(i) _(l) , . . . f_(i) _(k) } be a set of files accessed during

[T_(prev)^(mem), T_(prev)] interval, and let Reqs(T_(prev)) be a sequence of requests r₁,r₂, . . . , r_(n) accessing files Files (T_(prev)) during

[T_(prev)^(mem), T_(prev)] interval. As described above with FIG. 6, such accesses during the earlier

[T_(prev)^(mem), T_(prev)] interval may have previously been determined as including unique file segments having a total of bytes equal to the size Size^(mem) of the media server's memory. Thus, in block 802A, formula (2) from above may be used for each file f ε Files(T_(prev)) to consider all of the corresponding requests Regs^(f)(T_(prev))={r₁ ^(f), r₂ ^(f), . . . r_(n) ^(f)|r₁ ^(f)ε Reqs(T_(prev))}, and all of the unique segments of f which were accessed by requests from Regs^(f)(T_(prev)) in

[T_(prev)^(mem), T_(cur)] interval are determined.

It should be recognized that if accesses during the

[T_(prev)^(mem), T_(prev)] interval have previously been determined as including unique file segments having a total of bytes equal to the size Size^(mem) of the media server's memory, accesses that are older than

T_(prev)^(mem) need not be considered in identifying the accesses that correspond to the content of the media server's memory at time T_(cur). However, accesses from time T_(prev) to time T_(cur) are to be taken into consideration. Thus, all of the unique segments of file f which were accessed by requests from Reqs^(f)(T_(prev)) in

[T_(prev)^(mem), T_(cur)] interval are identified in block 802A.

In block 802B, the determined unique segments of interval

[T_(prev)^(mem), T_(cur)] are stored to a data structure called

FileTable(T_(prev)^(mem), T_(cur)) (as described above) using file structure ordering of the segments. In block 802C, the determined unique segments of interval

[T_(prev)^(mem), T_(cur)] are also stored to a data structure called

TimeFileTable(T_(prev)^(mem), T_(cur)) (as described above) using timestamp ordering of the segments.

In operational block 802D, the

FileTable(T_(prev)^(mem), T_(cur)) data structure is used to compute the total amount of unique bytes accessed during the

[T_(prev)^(mem), T_(cur)] time interval as:

${{UniqueBytes}\left( {T_{prev}^{mem},T_{cur}} \right)} = {\sum\limits_{f \in F_{prev}}{{{UniqueBytes}\left( {f,T_{prev}^{mem},T_{cur}} \right)}.}}$ Thus,

UniqueBytes(T_(prev)^(mem), T_(cur)) provides the total number of unique bytes of file segments accessed during the time interval

[T_(prev)^(mem), T_(cur)]. Of course, such number of unique bytes may exceed the amount that the media server's memory is capable of storing. Thus, in block 802E, the difference

UniqueBytes(T_(prev)^(mem), T_(cur)) − Size^(mem) is computed to determine the amount that

T_(prev)^(mem) should be advanced to a new time point

T_(cur)^(mem) such that the amount of unique bytes accessed between the determined

T_(cur)^(mem) and T_(cur) equals Size^(mem).

Operation then advances to block 803 whereat the unique file segments and corresponding access timestamps for file accesses in the determined time interval

[T_(cur)^(mem), T_(cur)] are determined, which provides the current memory state of the media server's memory (as of time T_(cur)). As shown in block 803A, the operation of block 803 may comprise updating data structures TimeFileTable and FileTable for time interval

[T_(cur)^(mem), T_(cur)] to contain only file segments starting at time

T_(cur)^(mem) and later.

In operational block 804, the determined current memory state may be used to determine the type of access for the newly received request

r_(new)^(f). That is, from the determined memory state, a determination can be made as to whether file f (or its initial prefix) requested by

r_(new)^(f) is residing in memory, and thus whether the request

r_(new)^(f) will have a type of access to memory or disk correspondingly. For instance, block 804 may comprise the operation of block 804A in which it is determined whether at least a prefix of the requested filed is included in the unique file segments of the current memory state, wherein if at least a prefix of the file f is included therein then

r_(new)^(f) is determined to be a memory access type and otherwise

r_(new)^(f) is determined to be a disk access type.

Operation may then advance to block 805 where it is determined whether a new request is received. As shown, operation may wait at block 805 for a time at which a new request is received. Upon a new request being received, operation advances from block 805 to block 806 whereat T_(cur) is set to the current time at which the new request is received, and

T_(prev)^(mem) is set to

T_(cur)^(mem) (i.e., the previously determined

T_(cur)^(mem) now becomes

T_(prev)^(mem)). Operation then returns to block 802 to compute the new

[T_(cur)^(mem), T_(cur)] time interval that includes file accesses with the amount of unique bytes equaling the size Size^(mem), determine the new memory state in block 803, and determine the type of access for the newly received request in block 804.

C. Cost-aware Admission Control for a Media Server

Embodiments of the present invention provide a cost-aware admission control strategy for streaming media servers that efficiently utilizes the available media server resources (capacity), while ensuring that desired QoS guarantees are maintained. In order to assign a cost to a newly received request, a determination is made as to whether the new request will be streaming data from memory (i.e., will have a cost of memory access) or will be accessing data from disk (i.e., will have a cost of disk access). It should be understood that assigning a “memory access” cost to a newly received request in certain embodiments does not assume or require that the whole file requested resides in memory. For instance, as described above with the example of FIG. 3, when there is a sequence of accesses to the same file, the first access may read the file from disk (to memory) while all of the subsequent requests access the corresponding file prefix from memory.

A satisfactory technique for measuring the capacity of a media server under realistic workloads is not available in the prior art. A standard commercial stress test used by most media server vendors measures a maximum number of concurrent streams deliverable by the server when all of the clients are accessing the same file encoded at a certain fixed bit rate, e.g. 500 Kb/s. That is, in standard commercial stress tests, vendors use a particular streaming media file that is encoded for transmission at a particular bit rate for measuring the maximum number of concurrent streams that clients can retrieve of this file.

The standard commercial stress test approach is unsatisfactory for several reasons. First, media files are often encoded for transmission at different bit rates. For instance, clients in a given population have different speed connections to the Internet (or other client-server network). For example, in a realistic population of clients, different clients typically comprise various different speed connections, such as dial-up modem connections (e.g., using a 28 or 56 kilobits analog modem), Integrated Services Digital Network (ISDN) connections, cable modem connections, Digital Subscriber Line (DSL) connections, and even higher-bandwidth connections, as examples. Accordingly, different clients may have different speed connections to the Internet varying, for example, from 28-kilobits (Kb) per second to 500 Kb (or more) per second, thus resulting in requirements for different bit rate encodings at the streaming media files being accessed by the different clients. That is, a media server may comprise streaming media files encoded for transmission at various different bit rates (e.g., 28 Kb/s, 56 Kb/s, etc.), and may attempt to serve the most appropriate encoded file to a client based at least in part on the client's connection speed to the Internet.

Additionally, clients typically may access different media files from the server. That is, a media server commonly provides a plurality of different media files, various ones of which may be accessed concurrently by different clients. When concurrent accesses of a single file is measured, it leads to measurement of the server's capacity for serving a streaming media file from memory, which is often not actually the case. Accordingly, the standard commercial stress tests used by most media server vendors are unsatisfactory for obtaining an accurate measurement of the capacity of a media server for supporting a realistic workload in serving streaming media files to clients.

As mentioned above, a technique for measuring server capacity using a cost function is disclosed in co-pending U.S. patent application Ser. No. 10/306,279 filed Nov. 27, 2002 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER,” the disclosure of which has been incorporated herein by reference. Also, a technique for measuring server capacity using a cost function is described by L. Cherkasova and L. Staley in “Building a Performance Model of Streaming Media Applications in Utility Data Center Environment”, Proc. of ACM/IEEE Conference on Cluster Computing and the Grid (CCGrid), May, 2003 (hereinafter referred to as “the L. Cherkasova Paper”), the disclosure of which is hereby incorporated herein by reference. The above references introduce a basic benchmark that can be used to establish the scaling rules for server capacity when multiple media streams are encoded at different bit rates. For instance, a basic benchmark may be executed for each of various different encoding bit rates for files stored at a media server. An objective of the basic benchmark according to one embodiment is to define how many concurrent streams of the same bit rate can be supported by the media server without degrading the quality of any streams. In accordance with one embodiment, the basic benchmark comprises two types of benchmarks:

-   -   1) Single File Benchmark measuring a media server capacity when         all the clients in the test are accessing the same file, and     -   2) Unique Files Benchmark measuring a media server capacity when         each client in the test is accessing a different file.

Thus, a Single File Benchmark (SFB) may be executed for each of various different encoding bit rates for files stored at a media server under evaluation. The SFB measures the media server capacity when all of the clients in the test are accessing the same file. That is, the result of the SFB for a particular encoding bit rate defines the maximum number of concurrent streams of a single file encoded at that particular bit rate that the media server can support. Example techniques for executing SFBs for a media server are described further in co-pending U.S. patent application Ser. No. 10/306,279 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER”

Similarly, a Unique File Benchmark (UFB) may be executed for each of various different encoding bit rates for files stored at a media server under evaluation. The UFB measures the media server capacity when all of the clients in the test are accessing different files. That is, the result of a UFB for a particular encoding bit rate defines the maximum number of concurrent streams, each of different files that are encoded at the particular bit rate, that the media server can support. Example techniques for executing UFBs for a media server are described further in co-pending U.S. patent application Ser. No. 10/306,279 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER.”

When all of a media server's clients are accessing a single file (as measured by the SFB), the media server is capable of serving the currently streamed bytes of the file from memory. However, when all of its clients are accessing a different file (as measured by the UFB), the media server serves each file from disk. Thus, the SFB is essentially a best-case scenario benchmark, whereas the UFB is essentially a worst-case scenario benchmark.

Using an experimental testbed with standard components available in a Utility Data Center environment and proposed set of basic benchmarks, the capacity and scaling rules of media server running RealServer 8.0 from RealNetworks was measured in the L. Cherkasova Paper. The measurement results reported in the L. Cherkasova Paper show that these scaling rules are non-trivial. For example, the difference between the highest and lowest bit rate of media streams used in those experiments was 18 times. However, the difference in maximum number of concurrent streams a server is capable of supporting for corresponding bit rates is only around 9 times for a Single File Benchmark, and 10 times for a Unique Files Benchmark. The media server performance is 2.5-3 times higher under the Single File Benchmark than under the Unique Files Benchmark. This quantifies the performance benefits for multimedia applications when media streams are delivered from memory.

As described in co-pending U.S. patent application Ser. No. 10/306,279 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER” and in the L. Cherkasova Paper, a set of basic benchmark measurements may be used to derive a cost function that defines a fraction of system resources needed to support a particular media stream depending on the stream bit rate and type of access (memory file access or disk file access), including the following costs:

-   -   A)

cost_(x_(i))^(memory) a value of cost function for a stream with memory access to a file encoded at X_(i) Kb/s; and

-   -   B)

cost_(x_(i))^(disk) —a value of cost function for a stream with disk access to a file encoded at X_(i) Kb/s.

Many admission control strategies proposed in the existing literature use the “fixed” maximum bandwidth a server is capable of delivering as the main “scheduling” resource for admission of a new stream. Evidently, the amount of bandwidth a server is capable of delivering is variable and depends on the encoding bit rates of current streams in progress and the access type: memory versus disk.

One goal of an admission control mechanism is to prevent a media server from becoming overloaded. Overloading a media server typically results in violation of real-time properties of the media applications that it supports. Typically, an overloaded media server continues to serve all of the accepted streams but the quality of service degrades: the packets for accepted streams are sent with violation of “on-time delivery” and in such a way that the quality of the stream received by a client is compromised.

An embodiment of the present invention provides an admission controller for managing admission of requests for streaming media files to a media server in a manner that enables optimum utilization of the media server's resources while ensuring that desired QoS guarantees are maintained. In accordance with one embodiment, the admission controller performs two main procedures when evaluating whether a new request

r_(new)^(f) can be accepted for service by a media server:

-   -   1) Resource Availability Check. During this procedure, a cost of         a new request

r_(new)^(f) is evaluated. To perform such evaluation, a current memory state of media server is computed using a segment-based access model in the manner described above. From the memory state, the admission controller can identify whether a prefix of requested file f is residing in memory, and thus determine whether request

r_(new)^(f) will have a cost of accessing memory or disk correspondingly. Then, the admission controller checks whether, in the current time (at which the request

r_(new)^(f) is received, the media server has enough available resources (capacity) to accommodate the resource requirements of such new request

r_(new)^(f); and

-   -   2) QoS Guarantees Check. During this procedure, the admission         controller verifies that the acceptance of request

r_(new)^(f) will not violate the QoS guarantees of already accepted requests at any point in the future, i.e., the media server will not enter an overloaded state at any point in the future.

Turning to FIG. 9, an example operational flow diagram of one embodiment of an admission controller is shown. In operational block 901, the admission controller receives a new request

r_(new)^(f) requesting service of a streaming media file from a media server. In operational block 902, the current memory state of the media server is determined (e.g., using the techniques described above for computing a current memory state). In operational block 903, a cost for the newly received request is determined. For instance, the type of access of the request (e.g., whether a memory access or a disk access) may be determined, and a corresponding cost^(memory) or cost^(disk) may be determined for the request. As described further herein, the determined memory state (of block 902) may be used in block 903 for determining the cost of

r_(new)^(f).

In operational block 904, the admission controller determines whether the media server has sufficient available resources to accommodate the newly received request

r_(new)^(f). That is, the computed cost of the newly received request is used to determined whether the media server's currently available resources are sufficient for servicing the request. If the media server does not have sufficient available resources to accommodate the newly received request, then operation advances to block 906 whereat the request is rejected (i.e., not admitted for service by the media server). Otherwise, if the media server has sufficient resources to accommodate the request, operation advances to block 905.

In operational block 905, the admission controller determines whether admission of the newly received request

r_(new)^(f) for service by the media server will cause violation of a desired QoS of any of the already active requests being serviced by the media server. If admission of the request

r_(new)^(f) will cause violation of a desired QoS of any of the already active requests, operation advances to block 906 whereat the request

r_(new)^(f) is rejected (i.e., not admitted for service by the media server). Otherwise, if it is determined that admission of the request

r_(new)^(f) will not cause a violation of the desired QoS for any of the already active requests, operation advances to block 907 whereat request

r_(new)^(f) is admitted for service by the media server.

In certain embodiments, a current memory state is computed for the media server in accordance with the techniques described above. From the memory state, the admission controller can identify, in response to a received request

r_(new)^(f), whether a requested file f or its initial prefix is residing in the media server's memory, and thus whether the request

r_(new)^(f) will have a cost of access to memory or disk correspondingly. If the media server has enough currently available capacity to accommodate the resource requirements of new request

r_(new)^(f), as determined in block 904 of FIG. 9, then request

r_(new)^(f) may be conditionally accepted. Otherwise, request

r_(new)^(f) is rejected.

When there is enough currently available server capacity to admit a new request

r_(new)^(f), an embodiment of the admission controller still ensures that the acceptance of request

r_(new)^(f) will not violate the QoS guarantees of already accepted requests over their duration and that the media server will not enter an overloaded state at any point in the future, such as in block 905 of FIG. 9. In particular, the admission controller assesses the following two situations:

-   -   1) Let a new request

r_(new)^(f) be a disk access. In this case, there is a continuous stream of new, additional bytes transferred from disk to memory (amount of new bytes is defined by the file f encoding bit rate). This may result in replacement (eviction) of some “old” file segments in memory, such as some segments of file {circumflex over (f)}, for example. If there is an active request r^({circumflex over (f)}) which reads the corresponding file segments from memory and has a cost of memory access, then once the corresponding segments of file {circumflex over (f)} are evicted (replaced) from memory, the request r^({circumflex over (f)}) will read the corresponding segments of file {circumflex over (f)} from disk with increased cost of disk access. Accordingly, the admission controller assesses such situations whenever they may occur in the future for already accepted (“active”) requests and evaluate whether the increased cost of impacted requests can be offset by the available capacity of the server at the corresponding time points; and

-   -   2) Let a new request

r_(new)^(f) be a memory access. The admission controller assesses whether the corresponding segments of file f may be evicted in some future time points by already accepted active disk requests, and whether the increased cost of request

r_(new)^(f) can be offset by the available capacity of the server at the corresponding time points. That is, if request

r_(new)^(f) is initially accepted as a memory access (e.g., the prefix of file f is available in memory at time T_(cur)), it is determined whether such request may (before it is fully serviced) become a disk access request and whether the media server will have sufficient resources to support such a disk access request.

Because certain embodiments of the admission controller attempt to ensure the QoS guarantees at any point in the future, an efficient procedure to advance the “system clock” and verify the QoS guarantees on a small set of future time points may be implemented in the manner described further below. It should be noted that an aim of the QoS validation process and related speculative computations of certain embodiments is to provide a determination of whether the request

r_(new)^(f) can be accepted or not for service by the media server and ensure a desired QoS for all accepted requests.

FIGS. 10A-10B show an example operational flow diagram for QoS validation that is performed by an admission controller in accordance with one embodiment of the present invention. For instance, such QoS validation process may be performed in block 905 of FIG. 9 in the overall operation of an embodiment of an admission controller. In accordance with the example embodiment for implementing QoS validation of FIGS. 10A-10B, in determining whether a received request

r_(new)^(f) can be accepted for service by the media server, the admission controller identifies “safely replaceable bytes” in memory. More particularly, in the example embodiment of FIGS. 10A-10B, all of the active requests ActReqs(T_(cur)) are partitioned in two groups:

-   -   1) ActMemReqs(T_(cur)): active memory requests, i.e. the         requests which have a memory access cost; and     -   2) ActDiskReqs(T_(cur)): active disk requests, i.e. the requests         which have a disk access cost.

For instance, in block 1001, all ActMemReqs(T_(cur)) are identified, and in block 1002 all ActDiskReqs(T_(cur)) are identified. The requests from ActMemReqs(T_(cur)) access their file segments in memory. Thus, they do not “bring” new bytes to memory—they only “refresh” the accessed file segments' timestamps with the current time. Only requests from ActDiskReqs(T_(cur)) bring new bytes from disk to memory and evict the corresponding amount of “oldest” bytes from memory.

There are simple dependencies between requests to the same file f. For instance, let Reqs(T_(cur))={r₁ ^(f), r₂ ^(f), . . . , r_(n) ^(f)} be a sequence of requests accessing file f in interval

[T_(cur)^(mem), T_(cur)] and let t^(start)(r₁ ^(f))≦t^(start)(r₂ ^(f))≦ . . . ≦t^(start)(r_(n) ^(f)) i.e. (r₁ ^(f)) is the oldest access and (r_(n) ^(f)) is the most recent access to a file f in

[T_(cur)^(mem), T_(cur)] interval. Let request r_(i) ^(f)(1≦i≦n) be a memory access. Since request r_(i) ^(f) reads the initial segment of file f from memory, and the most recent initial segment of file f in memory is written by the previous request r_(i−1) ^(f), there is the following dependency between accesses r_(i) ^(f) and r_(i−1) ^(f). The offset (t^(start)(r_(i) ^(f))−t^(start)(r_(i−1) ^(f))) between the arrival times of requests r_(n−1) ^(f) and r_(n) ^(f) indicates the timestamp of the file segment in memory, which currently is accessible by request r_(i) ^(f):

T_(cur)^(act_m)(r_(n)^(f)) = T_(cur) − (t^(start)(r_(i)^(f)) − t^(start)(r_(i − 1)^(f))).

As shown in operational block 1003, this example embodiment of the admission controller applies the above computation to each active memory request in ActMemReqs(T_(cur)):

$T_{cur}^{{act}\_ m} = {\min\limits_{r_{i}ɛ\;{{ActMemReqs}{(T_{cur})}}}{{T_{cur}^{{act}\_ m}\left( r_{i} \right)}.}}$

Time point

T_(cur)^(act_m) indicates the timestamp of the “oldest-active” portion in memory, which is currently used (read) by some active memory requests. Any active memory request r such that

T_(cur)^(act_m)(r) = T_(cur)^(act_m) is referred to as an active memory-oldest request.

In operational block 1004,

TimeFileTable(T_(cur)^(mem), T_(cur)), which provides time ordering of file segments in memory, is used to identify all the file segments stored between

T_(cur)^(mem)  and  T_(cur)^(act_m) and compute the corresponding amount of bytes in memory between these two time points as:

SafeReplBytes(T_(cur)) = UniqueBytes(T_(cur)^(mem), T_(cur)^(act_m)).

Thus, the computed SafeReplBytes(T_(cur)) are the bytes that can be safely replaced in memory (e.g., by new bytes being read from disk) without impacting any currently active memory requests. For instance, turning briefly to FIG. 11, an example of such safely replaceable bytes that may be determined is shown.

Returning to FIGS. 10A-10B, this example embodiment of an admission controller next logically advances the system clock. That is, a logical system clock is advanced to enable evaluation of the media server's performance at some time in the future. For instance, the logical system clock may be advanced to a particular point in future to determine how the media server will manage its load at that point in future. Thus, the admission controller may be forward-looking to anticipate the state of the media server at point(s) in the future, and can thus evaluate how the media server's future state will be impacted by actions taken currently, such as admitting a newly received request

r_(new)^(f), as described further below.

As shown in operational block 1005, the admission controller may use the information about file encoding bit rates as well as the future termination times for requests ActDiskReqs(T_(cur)) to compute a time duration At during which the requests from ActDiskReqs(T_(cur)) will either transfer from disk to memory the amount of bytes equal to SafeReplBytes(T_(cur)) or all of them terminate. It is safe to advance clock to time T_(cur)+Δt because during this time period the cost of accepted requests stays unchanged, and therefore the media server is guaranteed from entering the overloaded state in interval [T_(cur), T_(cur)+Δt]. In order to make the QoS validation process terminate within a limited number of steps, this embodiment attempts to advance the clock at each step either beyond the closest termination event or beyond a designated ClockAdvanceTime. ClockAdvanceTime is a parameter, which may be set depending, for example, on a workload presented to the media server. For instance, for a workload with long duration streams (e.g., long videos), ClockAdvanceTime might be set to 1 minute or higher, while for a workload with short duration streams, ClockAdvanceTime might be set, for example, to 5 seconds.

In operational block 1006, the admission controller determines if in interval [T_(cur), T_(cur)+Δt] all the requests from ActDiskReqs(T_(cur)) have reached their termination. If all of the requests from ActDiskReqs(T_(cur)) are determined in block 1006 to reach their termination in interval [T_(cur), T_(cur)+Δt], operation advances to block 1008 whereat request

r_(new)^(f) is admitted for service by the media server. Alternatively, operation advances to block 1007 and the admission controller determines if in interval [T_(cur), T_(cur)+Δt] all the requests from ActMemReqs(T_(cur)) have reached their termination. If all of the requests from ActMemReqs(T_(cur)) are determined in block 1007 to reach their termination in interval [T_(cur), T_(cur)+Δt], operation advances to block 1008 whereat request

r_(new)^(f) is admitted for service by the media server. More specifically, if either of the conditions of blocks 1006 and 1007 are found to be satisfied, then the media server is guaranteed from entering an overloaded state in the case of request

r_(new)^(f) being accepted, and thus such request is accepted for service in block 1008.

Otherwise, operation advances to block 1009 (via connector “A”) shown in FIG. 10B whereat the admission controller determines if Δt≧ClockAdvanceTime. If Δt≧ClockAdvanceTime, operation advances to block 1011, as described further below. Otherwise, operation advances to block 1010 whereat the admission controller determines whether there is one or more termination events within interval [T_(cur), T_(cur)+Δt]. If one or more termination events are determined within interval [T_(cur), T_(cur)+Δt], operation advances to block 1011, which is described further below—otherwise, operation advances from block 1010 to block 1014.

Let TermReqs(T_(cur), T_(cur)+Δt) denote all the requests which terminate in interval [T_(cur), T_(cur)+Δt]. In operational block 1011, the available server capacity is increased by cost (r_(i)) for each request r_(i)ε TermReqs(T_(cur),ΔT). Then, in operational block 1012, the admission controller computes an updated memory state and all of the corresponding data structures (e.g., the segment-based models) at time point T_(cur)+Δt. After that, the whole procedure is repeated with respect to T_(cur), =T_(cur)+Δt. Thus, in operational block 1013, time T_(cur) set to T_(cur)+Δt, and operation returns to block 1001 (via connector “B”) to repeat the above-described processes with respect to T_(cur)=T_(cur)+Δt.

If Δt≦ClockAdvanceTime (in operational block 1009) and there is no termination events within interval [T_(cur), T_(cur)+Δt] (in operational block 1010), operation advances to block 1014. Let ActMemOldestReqs(T_(cur))={r_(i), . . . , r_(k)} be a set of all active memory-oldest requests, i.e. for any

r_(i) : T_(cur)^(act_m)(r_(i)) = T_(cur)^(act_m), (1 ≤ i ≤ k). The active memory-oldest requests are accessing the file segments with the “oldest” timestamps in memory, and therefore, they are the most vulnerable to a situation that corresponding file segments can be evicted from memory. If this happens (i.e., their corresponding file segments are evicted from memory), these requests will incur the increased cost of disk access, and thus the admission controller attempts to identify those occurrences and ensure that there is enough available server capacity to offset this additional cost. Accordingly, in operational block 1014, for any r_(i)ε ActMemOldestReqs(T_(cur)) the admission controller assigns a corresponding cost of disk access (such process is referred to herein as a request cost update).

In operational block 1015, the admission controller determines whether there is sufficient available server capacity to offset this additional cost. If there is not enough available server capacity to offset the corresponding request cost updates, then the end of the QoS validation process is reached and request

r_(new)^(f) is rejected in block 1016. If, on the other hand, there is enough available server capacity to offset the corresponding request cost updates, then requests ActMemOldestReqs(T_(cur)) are removed from ActMemReqs(T_(cur)) and are added to ActDiskReqs(T_(cur)) in operational block 1017. And, in operational block 1018, the available server capacity is correspondingly updated (e.g., decreased). After that, time point

T_(cur)^(act_m) is recomputed with respect to an updated set of ActMemReqs(T_(cur)), and operation returns to block 1003 (via connector “C”) shown in FIG. 10A and the process continues for the updated data structures.

The QoS validation process described above in FIGS. 10A-10B guarantees to terminate and to provide the answer whether request

r_(new)^(f) should be accepted or not.

In an embodiment of the admission controller, two types of events may be encountered by the controller: 1) acceptance of new requests, and 2) termination of previously accepted (active) requests. In these time points, the amount of available server resources is reevaluated by the controller. For instance, in the event of an acceptance of a new request, additional server resources may be allocated for servicing the new request (thus decreasing the amount of available server resources), and in the event of termination of an active request, server resources allocated for servicing such terminated request may be released (thus increasing the amount of available server resources). It should be understood that multiple events may be encountered in the same time point. The following notations are used herein:

-   -   ActReqs(t): the set of requests that are currently in progress,         i.e. “active” at time t;     -   TermReqs(t): the set of requests that are suppose to terminate         at event time t;     -   Cap: the absolute server capacity. In an example embodiment, the         Cap is set to 1, and the cost function for requests is derived         using this setting. For example, if a media server can support N         concurrent disk accesses to files encoded at X_(i) Kb/s then the         cost

cost_(x_(i))^(disk) Of the disk request to a file encoded at

${X_{i}{{Kb}/s}\mspace{14mu}{is}\mspace{14mu}\frac{1}{N}};$ and

-   -   ACap(t): the available server capacity at time t. In the initial         time point, one may use ACap(T_(init))=0.9×Cap.

An embodiment of the admission controller handles events as described below. Let the current event time (i.e., a time at which an event is encountered or detected by the admission controller) be T_(cur). If there are events of both types: termination of some already accepted (active) requests and acceptance of a new request, then the termination event activities are performed first (the corresponding resources are released), and the acceptance event activities are performed after that. An example embodiment of the admission controller performs the actions described below depending on the event type.

First, let us consider the actions of an example embodiment of the admission controller for an acceptance of a new request event. To evaluate whether a new request

r_(new)^(f) can be accepted at time T_(cur), the admission controller performs the following two procedures: 1) Resource Availability Check and 2) QoS Guarantees Check, which are each described further below.

During the Resource Availability Check procedure, a cost of a new request

r_(new)^(f) is evaluated via computation of a current memory state of the media server as described above. In particular, this example embodiment of the admission controller computes a timestamp

T_(cur)^(mem) such that only file segments accessed at time

T_(cur)^(mem) and later are residing in memory. Additionally, for each request r ε ActMemReqs(T_(cur)), the admission controller computes timestamp

T_(cur)^(act_m)(r) corresponding to the oldest-active portion of memory which currently is accessible by the active memory requests. After that, the admission controller identifies if there are any requests r ε ActMemReqs(T_(cur)) such that

T_(cur)^(act_m)(r) ≤ T_(cur)^(mem). This situation reflects that while request r has been originally a memory access, the current memory state has changed and request r at time

T_(cur)^(mem) streams the corresponding bytes from disk. Hence, access r requires a corresponding cost update. Let UpdReqs denote all such requests (this set may be empty). Since the QoS validation process described above in conjunction with FIGS. 10A-10B guarantees that there is enough available server capacity in the corresponding time points, the admission controller runs a procedure of cost update for requests r ε UpdReqs: for any r ε UpdReqs, the admission controller assigns a corresponding cost of disk access, these requests are removed from ActMemReqs(T_(cur)) and are added to ActDiskReqs(T_(cur)), and the available server capacity is correspondingly decreased. Then, the admission controller checks whether the media server still has enough available resources (capacity) to accommodate the resource requirements of a new request

r_(new)^(f). In case of a positive outcome (i.e., the admission controller determines that the media server still has sufficient capacity to service the new request), the admission controller moves on to the QoS validation process, described below.

During the QoS Guarantees Check procedures, this example embodiment of the admission controller verifies that by accepting the new request

r_(new)^(f) the media server will not enter an overloaded state at any point in the future, as described above in conjunction with FIGS. 10A-10B. If the outcome of this QoS validation process is positive (i.e., it is determined that the media server will not enter an overloaded state at any point in the future), then new request

r_(new)^(f) is accepted and the following actions are performed:

-   -   the available server capacity is decreased by the

$\begin{matrix} {{{{{cost}\left( r_{new}^{f} \right)}\text{:}\mspace{14mu} A\;{{Cap}\left( T_{cur} \right)}} = {{A\;{{Cap}\left( T_{cur} \right)}} - {{cost}\left( r_{new}^{f} \right)}}};} \\ {{{{ActReqs}\left( T_{cur} \right)} = {{{ActReqs}\left( T_{cur} \right)}\bigcup r_{new}^{f}}};{and}} \\ {{{{let}\mspace{14mu} T^{\prime}} = {T_{cur} + {{duration}\left( r_{new}^{f} \right)}}},{{{then}\mspace{14mu}{{TermReqs}\left( T^{\prime} \right)}} = {{{TermReqs}\left( T^{\prime} \right)}\bigcup{r_{new}^{f}.}}}} \end{matrix}$

Now let us consider the actions of an example embodiment of the admission controller for termination of currently accepted (active) requests. In the time points corresponding to termination events, the following actions are performed:

-   -   ActReqs(T_(cur))=ActReqs(T_(cur))\TermReqs(T_(cur)); and     -   The server capacity is increased by the cost of the terminated         requests:

${A\;{{Cap}\left( T_{cur} \right)}} = {{A\;{{Cap}\left( T_{cur} \right)}} + {\sum\limits_{r \in {{TermReqs}{(T_{cur})}}}{{{cost}(r)}.}}}$

The above describes an example implementation of an admission controller in accordance with an embodiment of the present invention. Utilizing the techniques described above, a cost-aware admission controller may be implemented to manage the admission of requests to a streaming media server, which provides desirable performance benefits over traditional admission techniques for streaming media servers. In order to evaluate the performance benefits of such a cost-aware admission controller, we constructed a simulation model of the above-described example implementation of an admission controller in Java. Using this simulation model, we compared the example admission controller's performance against the traditional pessimistic strategy for a streaming media server. The pessimistic admission control strategy is based on measuring the media server capacity as a number of concurrent streams the disk subsystem is capable of supporting. When a new request arrives, it is assigned the cost of disk access. If there is enough available capacity to accommodate the resource requirements of a new request, it is accepted.

For workload generation, we used the publicly available, synthetic media workload generator known as Medisyn. We performed a sensitivity study, using two workloads closely imitating typical parameters of real enterprise media server workloads. The overall statistics for Workload 1 and Workload 2 used in the study are summarized in Table 1 below:

TABLE 1 Workload parameters used in simulation study. Workload 1 Workload 2 Number of Files 800 800 Zipf α 1.34 1.22 Storage Requirement 41 GB 41 GB Number of Requests 41,703 24,159

Both synthetic workloads have the same media file duration distribution, which &an be briefly summarized via six following classes: 20% of the files represent short streams (e.g., videos) having duration 0-2 minutes, 10% of the streams have duration of 2-5 minutes, 13% of the streams have duration of 5-10 minutes, 23% of the streams have duration of 10-30 minutes, 21% of the streams have duration of 30-60 minutes, and 13% of the streams have durations longer than 60 minutes.

The file bit rates are defined by the following discrete distribution: 5% of the files are encoded at 56 Kb/s, 20% of the files are encoded at 112 Kb/s, 50% of the files are encoded at 256 Kb/s, 20% of the files are encoded at 350 Kb/s, and 5% of the files are encoded at 500 Kb/s. The file popularity is defined by a Zipf-like distribution with a shown in Table 1. In summary, Workload 1 has a higher locality of references than Workload 2: 90% of the requests target 10% of the files in Workload 1 compared to 90% of the requests targeting 20% of the files in Workload 2. Correspondingly, 10% of the most popular files in Workload 1 have an overall combined size of 3.8 GB, while 20% of the most popular files in Workload 2 use 7.5 GB of storage space.

Session arrivals are modeled by a Poisson process with rate corresponding to the media server overload. We performed a set of simulations for a media server with different memory sizes of 0.5 GB, 1 GB, 2 GB, and 4 GB. We defined the server capacity and the cost functions similar to those measured using the experimental testbed described above for deriving cost functions. In our experiment, we used

cost_(x_(i))^(disk)/cost_(x_(i))^(memory) = 3, i.e. the cost of disk access is 3 times higher than the cost of the corresponding memory access to the same file.

The performance results for both workloads from our experiment are shown in FIGS. 12A (showing results for Workload 1) and 12B (showing results for Workload 2). They represent a normalized throughput improvement under the example cost-aware admission controller compared to the pessimistic admission control strategy using two metrics: 1) the number of accepted requests and 2) the total number of transferred bytes. As can be seen, the example cost-aware admission controller significantly outperforms the pessimistic strategy for both workloads while providing the hard QoS guarantees for media service in the manner described above. The media server's performance can be significantly improved via efficient memory management even for a media server with a relatively small memory size. Despite the fact that Workload 2 has much less reference locality and its popular files occupy twice as much space compared to that of Workload 1 in our experiment, the performance improvements under the cost-aware admission controller for Workload 2 are only slightly lower than for Workload 1.

Embodiments of the present invention may be utilized to model the memory of a media server. Such a memory model may be used, for example, in implementing an admission control policy for the media server for managing the acceptance of client requests to be serviced by the media server (e.g., to ensure optimal resource utilization and/or a desired level of quality of service). For instance, the memory of a media server may be modeled during its runtime, and such memory model may be utilized for measuring the capacity of the media server in supporting actual workloads applied thereto by a population of clients. For example, the capacity of the media server may be measured in accordance with the teachings of co-pending U.S. patent application Ser. No. 10/306,279 titled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER,” using the memory model for determining the current memory state of the media server. Thus, when actually implemented in a client-server network, a media server's capacity (e.g., its available capacity) may be monitored for supporting actual workloads applied thereto using a derived cost function, as described in co-pending U.S. patent application Ser. No. 10/306,279 titled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER.” This is particularly attractive in systems in which resources may be dynamically allocated, such as in Utility Data Centers (UDCs), for supporting the applied workloads.

It should be understood that while the example embodiments described above use the cost functions, as derived in co-pending U.S. patent application Ser. No. 10/306,279 entitled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER” and in the L. Cherkasova Paper, application of the present invention is not intended to be limited solely to those cost functions. Rather, in alternative embodiments the memory modeling approach described herein may be used for implementing other admission control strategies now known or later developed, including other cost-aware admission control strategies.

FIG. 13 shows an example UDC 1300 in which resources may be dynamically allocated. Such a UDC 1300 may be used for implementing a media server complex in which resources are dynamically allocated for the media server responsive to the workload applied thereto in accordance with the measured capacity of the media server. Implementations of UDC 1300 are known in the art and therefore UDC 1300 is only briefly described herein. As shown in FIG. 13, UDC 1300 comprises data center management logic 1301 that is operable to manage the allocation of resources in UDC 1300. UDC 1300 is coupled to a communications network, such as the Internet 1302 and/or Intranets 1303, thus enabling access by clients (not shown) via such communication networks. Network virtualization logic 1304 and storage virtualization logic 1305 is also included. UDC 1300 further comprises cluster pool 1306, network-attached storage (NAS) pool 1307, load balancer pool 1308, firewall pool 1309, and storage pool 1310. Again, data center management logic 1301 is operable to manage the allocation of resources, such as resources available in cluster pool 1306, NAS pool 1307, and storage pool 1310. Thus, by modeling the current memory state of a media server in accordance with embodiments of the present invention and using such memory state for computing a cost function for measuring the capacity of the media server complex under an applied workload as described in co-pending U.S. patent application Ser. No. 10/306,279 titled “SYSTEM AND METHOD FOR MEASURING THE CAPACITY OF A STREAMING MEDIA SERVER,” data center management logic 1301 may, responsive to the measured capacity, dynamically allocate the appropriate resources for supporting the applied workload.

FIG. 14 illustrates an example computer system 1400 adapted according to embodiments of the present invention. That is, computer system 1400 comprises an example system on which embodiments of the present invention may be implemented. Central processing unit (CPU) 1401 is coupled to system bus 1402. CPU 1401 may be any general purpose CPU. The present invention is not restricted by the architecture of CPU 1401 as long as CPU 1401 supports the inventive operations as described herein. CPU 1401 may execute the various logical instructions according to embodiments of the present invention. For example, CPU 1401 may execute machine-level instructions according to the exemplary operational flows described above in conjunction with FIGS. 4, 7, 8, 9, 10A, and 10B. For instance, example computer system 140Q may comprise a media server for implementing the above-described operations of an embodiment of the present invention, or example computer system 1400 may comprise an admission controller that is included in a media server or is communicatively coupled to a media server for implementing the above-described operations of an embodiment of the present invention, as examples.

Computer system 1400 also preferably includes random access memory (RAM) 1403, which may be SRAM, DRAM, SDRAM, or the like. Computer system 1400 preferably includes read-only memory (ROM) 1404 which may be PROM, EPROM, EEPROM, or the like. RAM 1403 and ROM 1404 hold user and system data and programs, as is well known in the art.

Computer system 1400 also preferably includes input/output (I/O) adapter 1405, communications adapter 1411, user interface adapter 1408, and display adapter 1409. I/O adapter 1405, user interface adapter 1408, and/or communications adapter 1411 may, in certain embodiments, enable a user to interact with computer system 1400 in order to input information thereto.

I/O adapter 1405 preferably connects storage device(s) 1406, such as one or more of hard drive, compact disc (CD) drive, floppy disk drive, tape drive, etc. to computer system 1400. The storage devices may be utilized when RAM 1403 is insufficient for the memory requirements associated with storing data for application programs. Communications adapter 1411 is preferably adapted to couple computer system 1400 to network 1412 (e.g., network 103 of FIG. 1).

User interface adapter 1408 couples user input devices, such as keyboard 1413, pointing device 1407, and microphone 1414 and/or output devices, such as speaker(s) 1415 to computer system 1400. Display adapter 1409 is driven by CPU 1401 to control the display on display device 1410.

It shall be appreciated that the present invention is not limited to the architecture of system 1400. For example, any suitable processor-based device may be utilized, including without limitation personal computers, laptop computers, computer workstations, and multi-processor servers. Moreover, embodiments of the present invention may be implemented on application specific integrated circuits (ASICs) or very large scale integrated (VLSI) circuits. In fact, persons of ordinary skill in the art may utilize any number of suitable structures capable of executing logical operations according to the embodiments of the present invention. 

1. A method for managing admission of requests to a streaming media server, the method comprising: receiving a new request for a streaming media file to be served by a streaming media server; performing a resource availability check for the streaming media server to determine whether the streaming media server has sufficient available resources to service the new request, wherein performing said resource availability check includes using a segment-based memory model to determine whether at least a portion of the requested streaming media file is in streaming media server's memory; and performing a quality of service guarantee check for the streaming media server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the streaming media server for any previously accepted requests.
 2. The method of claim 1 further comprising: determining from the segment-based memory model a cost associated with serving the requested streaming media file from the streaming media server.
 3. The method of claim 1 wherein said resource availability check comprises: determining a cost associated with serving the requested streaming media file from the streaming media server.
 4. The method of claim 3 wherein the cost comprises; a cost of serving the requested streaming media file either from memory or from disk.
 5. The method of claim 1 wherein said resources comprise memory resources and disk resources.
 6. The method of claim 1 wherein said sufficient available resources to service the new request comprises sufficient resources available so as not to overload the streaming media server.
 7. The method of claim 1 wherein said desired quality of service comprises real-time delivery of streaming media files requested by said previously accepted requests.
 8. The method of claim 1 further comprising: if determined that the streaming media server has sufficient available resources to service the new request and determined that acceptance of the new request will not violate, at any point in the future, said desired quality of service provided by the streaming media server for any previously accepted requests, then the streaming media server serving the requested streaming media file for said new request.
 9. The method of claim 1 further comprising: if determined that the streaming media server does not have sufficient available resources to service the new request or determined that acceptance of the new request will violate, at any point in the future, said desired quality of service provided by the streaming media server for any previously accepted requests, then rejecting the new request for service by the streaming media server.
 10. The method of claim 1 wherein said performing said quality of service guarantee check comprises: performing said quality of service guarantee check even when determined by said resource availability check that the streaming media server has sufficient available resources to service the new request.
 11. A method for managing admission of requests to a media server, the method comprising: receiving a new request for a streaming file to be served by a media server; determining a cost to the media server for serving the requested streaming file, wherein the cost corresponds to the media server's resources to be consumed in serving the requested streaming file, and wherein said determining said cost comprises determining a segment-based memory model that identifies content of the media server's memory as of a time that the new request is received, and using the segment-based memory model to determine whether at least a portion of the requested streaming file is in the media server's memory; and determining, based at least in, part on the cost, whether to admit the new request for service by the media server.
 12. The method of claim 11 wherein the cost comprises: a cost of serving the requested streaming file from memory if determined that the requested streaming file is in the media server's memory and a cost of serving the requested streaming file from disk if determined that the requested streaming file is not in the media server's memory.
 13. The method of claim 11 wherein said determining whether to admit the new request for service by the media server comprises: performing a resource availability check for the media server to determine whether the media server has sufficient available resources to service the new request.
 14. The method of claim 13 wherein said sufficient available resources to service the new request comprises sufficient resources available so as not to overload the media server.
 15. The method of claim 13 wherein said determining whether to admit the new request for service by the media server further comprises: performing quality of service guarantee check for the media server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the media server for any previously accepted requests.
 16. A system comprising: server having a memory, wherein said server is operable to serve at least one streaming file to clients communicatively coupled thereto; and an admission controller operable to receive a new request for a streaming file to be served by said server, determine a cost to the server for serving the requested streaming file, wherein the cost corresponds to the server's resources to be consumed in serving the requested streaming file, and determine, based at least in part on the cost, whether to admit the new request for service by the server; wherein said admission controller is further operable to determine a segment-based memory model that identifies content of the server's memory as of a time that the new request is received, and said admission controller is operable to use the segment-based memory model to determine whether at least a portion of the requested streaming file is in the server's memory.
 17. The system of claim 16 wherein the cost comprises: a cost of serving the requested streaming file from memory if determined that the requested streaming file is in the server's memory and a cost of serving the requested streaming file from disk if determined that the requested streaming file is not in the server's memory.
 18. The system of claim 16 wherein said admission controller is further operable to perform a resource availability check for the server to determine whether the server has sufficient available resources to service the new request.
 19. The system of claim 18 wherein said sufficient available resources to service the new request comprises sufficient resources available so as not to overload the server.
 20. The system of claim 16 wherein said admission controller is further operable to perform quality of service guarantee check for the server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the server for any previously accepted requests.
 21. The system of claim 16 wherein said admission controller determines the cost to the server for serving the requested streaming file based at least in part on the determined segment-based memory model.
 22. A method comprising: receiving, at a time T_(cur), a new request for a streaming file to be served by a media server; creating a segment-based model of the media server's memory as of time T_(cur); and based at least in part on the segment-based model of the media server's memory, determining whether to accept the received request for service by the media server.
 23. The method of claim 22 wherein said segment-based model of the media server's memory comprises (a) identification of unique segments of streaming files previously accessed by clients of the media server and (b) identification of corresponding timestamps of most recent accesses of each unique segment.
 24. The method of claim 22 wherein said determining whether to accept the received request for service by the media server comprises: determining whether the received request can be serviced by the media server without overloading the media server.
 25. The method of claim 22 wherein said determining whether to accept the received request for service by the media server comprises: determining a cost to the server for serving the requested streaming file, wherein the cost corresponds to the amount of the media server's resources to be consumed in serving the requested streaming file.
 26. The method of claim 22 wherein said determining whether to accept the received request for service by the media server comprises: performing a resource availability check for the media server to determine whether the media server has sufficient available resources to service the new request.
 27. The method of claim 22 wherein said determining whether to accept the received request for service by the media server comprises: performing quality of service guarantee check for the media server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the media server for any previously accepted requests.
 28. A non-transitory computer readable medium having computer executable instructions stored thereon which are executed by a processor to cause a computing device to perform a method, the method comprising: creating a segment-based model of a media server's memory; and determining whether to serve a requested streaming file from the media server based at least in part on the segment-based model of the media server's memory.
 29. The computer readable medium of claim 28, the method further comprising: receiving a request for said streaming file.
 30. The computer readable medium of claim 29, the method further comprising: responsive to receiving said request, determining whether to accept the request for service by the media server.
 31. The computer readable medium of claim 30 wherein determining whether to accept the request for service by the media server comprises: determining whether the request can be serviced by the media server without overloading the media server.
 32. The computer readable medium of claim 28 wherein said segment-based model of the media server's memory comprises (a) identification of unique segments of streaming files previously accessed by clients of the media server and (b) identification of corresponding timestamps of most recent accesses of each unique segment.
 33. The computer readable medium of claim 28 wherein determining whether to serve a requested streaming file from the media server comprises: determining a cost to the media server for serving the requested streaming file, wherein the cost corresponds to the amount of the media server's resources to be consumed in serving the requested streaming file.
 34. The computer readable medium of claim 28 wherein determining whether to serve a requested streaming file from the media server comprises: performing a resource availability check for the media server to determine whether the media server has sufficient available resources to service the new request.
 35. The computer readable medium of claim 28 wherein determining whether to serve a requested streaming file from the media server comprises: performing quality of service guarantee check for the media server to determine whether acceptance of the new request will violate, at any point in the future, a desired quality of service provided by the media server for any previously accepted requests.
 36. A cost-aware admission control system comprising: means for receiving, at a time T_(cur), a new request for a streaming file to be served by a media server; means for creating a segment-based model of the media server's memory as of time T_(cur); and means for determining, based at least in part on the segment-based model of the media server's memory, whether to accept the received request for service by the media server.
 37. The cost-aware admission control system of claim 36 wherein said segment-based model of the media server's memory comprises (a) identification of unique segments of streaming files previously accessed by clients of the media server and (b) identification of corresponding timestamps of most recent accesses of each unique segment.
 38. The cost-aware admission control system of claim 36 wherein said means for determining whether to accept the received request for service by the media server comprises: means for determining whether the received request can be serviced by the media server without overloading the media server.
 39. The cost-aware admission control system of claim 36 wherein said means for determining whether to accept the received request for service by the media server comprises: means for determining a cost to the server for serving the requested streaming file, wherein the cost corresponds to the amount of the media server's resources to be consumed in serving the requested streaming file. 